i

CHRONOLOGICAL AGE, DENTAL AGE AND NUTRITIONAL STATUS

AMONG 3-5-YEAR-OLDS WITH EARLY CHILDHOOD CARIES IN

NAIROBI, KENYA.

V60/87644/2016: JOYCE ATIENO OMUOK, BDS (UoN).

DISSERTATION IN PARTIAL FULFILMENT FOR A MASTERS OF

DENTAL SURGERY DEGREE IN PAEDIATRIC DENTISTRY OF THE

UNIVERSITY OF NAIROBI.

NOVEMBER, 2020.

ii

DECLARATION

I, Omuok Joyce Atieno, declare that this is my original work, and it has not been

submitted in any other institution for the award of any degree.

Signature …………………………………… Date…….……………………

iii

DEDICATION

In memory of my mother, Elizabeth, without whom I would not be where I am today.

iv

SUPERVISORS’ APPROVAL

We have approved this research work as supervisors of the University of Nairobi.

GLADYS N. OPINYA, BDS (UoN), CAGS, MSc.D (BOSTON USA), PhD (UoN)

Professor of Paediatric Dentistry, Department of Paediatric Dentistry and

Orthodontics,

School of Dental Sciences, University of Nairobi.

Signature……………………………….……… Date…………………………

EDITH NGATIA, BDS (UoN), MSc (UoN)

Public Health Nutritionist & Dental Surgeon,

Senior Lecturer, Human Nutrition, Department of Paediatric Dentistry and

Orthodontics,

School of Dental Sciences, University of Nairobi.

Signature………………………………………. Date…………………………

v

ACKNOWLEDGEMENT

I want to thank my supervisors Prof. Opinya and Dr Ngatia for their excellent

support and guidance during this thesis project. Much appreciation to Dr T. J.

Ocholla for his expert assistance in dental age assessment, and not forgetting Mr.

Chege and Mr. Ruto for their tremendous help with the taking of radiographs. I

extend my heartfelt gratitude to my research assistants Celine and Brenda. I wish to

appreciate Mr Aggrey Mokaya for data management and analysis. Moreover, finally

a big thank you to the entire staff of Lady Northey Dental Clinic together with their

patients for their generosity.

vi

TABLE OF CONTENTS

DECLARATION ........................................................................................................ ii

DEDICATION ...........................................................................................................iii

SUPERVISORS’ APPROVAL ................................................................................ iv

ACKNOWLEDGEMENT ......................................................................................... v

TABLE OF CONTENTS .......................................................................................... vi

LIST OF FIGURES ................................................................................................xiii

LIST OF ABBREVIATIONS ................................................................................. xv

DEFINITION OF TERMS ..................................................................................... xvi

ABSTRACT ............................................................................................................ xvii

CHAPTER ONE ........................................................................................................ 1

1.0 INTRODUCTION AND LITERATURE REVIEW ......................................... 1

1.1 INTRODUCTION .............................................................................................. 1

1.2 LITERATURE REVIEW .................................................................................. 2

1.2.1 EARLY CHILDHOOD CARIES ....................................................................... 2

1.2.1.1 Definition of early childhood caries (ECC) ................................................. 2

1.2.1.2 The prevalence of early childhood caries in Kenya ..................................... 2

1.2.1.3 Consequences of early childhood caries ...................................................... 2

1.2.2 NUTRITIONAL STATUS.................................................................................. 3

1.2.2.1 Definition of nutrition and nutritional status ................................................ 3

1.2.2.2 Factors affecting the nutritional status of children aged 5 years and under . 3

1.2.2.3 Methods of assessing nutritional status ........................................................ 3

1.2.3 CHRONOLOGICAL AGE ................................................................................. 4

1.2.3.1 Methods of assessing chronological age ...................................................... 5

1.2.4 DENTAL MATURITY AND DENTAL AGE ................................................... 5

1.2.4.1 The need for dental age estimation .............................................................. 7

1.2.4.2 Imaging and dental age estimation ............................................................... 7

1.2.4.3 Classification of dental age estimation methods ......................................... 8

1.2.4.4 Estimation of dental age in children and adolescents. ................................. 8

1.2.4.5 The Demirjian Method of Dental Age Estimation ....................................... 9

vii

1.2.5 THE RELATIONSHIP BETWEEN CHRONOLOGICAL AGE, DENTAL

AGE, NUTRITIONAL STATUS AND EARLY CHILDHOOD CARIES .............. 11

1.2.5.1 Early childhood caries and nutritional status ............................................. 11

1.2.5.2 Early childhood caries and dental age ........................................................ 13

1.2.5.3 Nutritional status and dental age ................................................................ 14

1.2.5.4 Chronological age and dental age .............................................................. 14

1.3 STATEMENT OF THE PROBLEM ............................................................... 15

1.4 JUSTIFICATION OF THE STUDY ................................................................ 15

1.5 STUDY OBJECTIVES ...................................................................................... 16

1.5.1 Main Objective .............................................................................................. 16

1.5.2 Specific objectives ........................................................................................ 16

1.6 HYPOTHESES ................................................................................................... 16

1.6.1 Null hypotheses ............................................................................................. 16

1.7 VARIABLES ...................................................................................................... 17

1.7.1 Independent variables.................................................................................... 17

1.7.2 Dependent variables ...................................................................................... 17

1.7.3 Sociodemographic variables ......................................................................... 17

1.7.4 Confounding variables .................................................................................. 17

CHAPTER TWO ..................................................................................................... 18

2.0 MATERIALS AND METHODS ...................................................................... 18

2.1 STUDY AREA AND POPULATION .............................................................. 18

2.1.1 Study area ...................................................................................................... 18

2.1.2 Study population ........................................................................................... 18

2.1.2.1 Inclusion criteria......................................................................................... 18

2.1.2.2 Exclusion criteria ....................................................................................... 18

2.2 METHODOLOGY ............................................................................................. 19

2.2.1 Study design .................................................................................................. 19

2.2.2 Sampling technique ....................................................................................... 19

2.2.3. Determination of the sample size ................................................................. 19

2.3 DATA COLLECTION AND ANALYSIS ....................................................... 20

viii

2.3.1 Data collection instruments ........................................................................... 20

2.3.2 Chronological age ......................................................................................... 20

2.3.3 Dental caries experience ............................................................................... 20

2.3.4 Oral hygiene status ........................................................................................ 20

2.3.5 Nutritional status ........................................................................................... 20

2.3.6 Dental age...................................................................................................... 21

2.4 DATA VALIDITY AND RELIABILITY ........................................................ 21

2.4.1 Pretesting of data collection tool ................................................................... 21

2.4.2 Calibration of the principal investigator ....................................................... 21

2.5 DATA ANALYSIS AND PRESENTATION ................................................... 22

2.6 MINIMISATION OF BIAS AND ERRORS ................................................... 22

2.7 ETHICAL CONSIDERATIONS ...................................................................... 22

2.8 STUDY LIMITATIONS .................................................................................... 23

CHAPTER THREE ................................................................................................. 24

3.0 RESULTS ........................................................................................................... 24

3.1 SOCIO-DEMOGRAPHICS .............................................................................. 24

3.1.1 Distribution of the caregiver‘s marital status and the person who provided

care: ........................................................................................................................ 24

3.1.2 The caregiver‘s level of education and the type of job: .................................... 25

3.1.3 Natal History ................................................................................................. 26

3.1.4 Feeding Habits during the infancy period ..................................................... 27

3.1.5 Distribution of the children by gender and age categories ............................ 28

3.2 CHRONOLOGICAL AGE ............................................................................... 29

3.2.1 Chronological age by gender ........................................................................ 29

3.2.2 Chronological ages by age groups ................................................................ 30

3.3 ESTIMATED DENTAL AGE .......................................................................... 31

3.3.1 The mean estimated age by age group .......................................................... 31

3.3.2 The mean estimated dental age by gender .................................................... 32

3.3.3 Estimated Dental Age Delayed and Not Delayed ......................................... 33

3.4 ORAL HYGIENE STATUS .............................................................................. 34

ix

3.4.1 Plaque score and gingival index .................................................................... 34

3.4.2 Oral hygiene status by age ............................................................................ 35

3.4.3 The severity of gingivitis by age and gender ................................................ 36

3.4.3.1 Age: ............................................................................................................ 36

3.4.3.2 Gender: ....................................................................................................... 36

3.4.4 Oral hygiene and ECC .................................................................................. 37

3.5 EARLY CHILDHOOD CARIES ..................................................................... 38

3.5.1 Severity of ECC in the study group .............................................................. 38

3.5.2 Gender and ECC ........................................................................................... 38

3.5.3 Chronological age and ECC .......................................................................... 39

3.5.4 Dental maturity and ECC .............................................................................. 39

3.5.5 Estimated dental age and ECC ...................................................................... 40

3.5.5.1 Hypothesis .................................................................................................. 40

3.6 NUTRITIONAL STATUS ................................................................................ 40

3.6.1 Weight for Height Z Scores .......................................................................... 40

3.6.1.1 All Children Weight for Height Z Scores .................................................. 40

3.6.1.2 Weight for Height Z Scores by age group ................................................. 41

3.6.1.3. Weight for Height Z Scores by gender ..................................................... 42

3.6.2 Weight for Age Z Scores............................................................................... 43

3.6.2.1 All Children Weight for Age Z Scores ...................................................... 43

3.6.2.2 Weight for Age Z Scores by age group ...................................................... 44

3.6.2.3 Weight for age Z Scores by gender ............................................................ 45

3.6.3 Height for Age Z Scores ............................................................................... 46

3.6.3.1 All Children Height for Age Z Scores ....................................................... 46

3.6.3.2 Height for Age Z Scores by age group ...................................................... 47

3.6.3.3 Height for Age Z Scores by gender ........................................................... 48

3.6.4 Nutritional Status and ECC ........................................................................... 49

3.6.4.1. Weight for Height Z Scores and Severity of ECC .................................... 49

3.6.4.2 Weight for Age Z scores and Severity of ECC .......................................... 50

3.6.4.3 Height for Age Z Scores and the Severity of ECC .................................... 50

x

3.6.4.4 Statistical test ............................................................................................. 51

3.6.4.5 Hypothesis .................................................................................................. 51

3.6.5 Nutritional Status and Dental Age ................................................................ 52

3.6.5.1 Weight for Height for Z scores and dental age ......................................... 52

3.6.5.1.1 Statistical test: independent samples test ................................................. 52

3.6.5.2 Weight for age Z score and dental age ........................................................ 52

3.6.5.2.1 Statistical test: independent samples test ................................................. 53

3.6.5.3 Height for age Z score and dental age ......................................................... 53

3.6.5.3.1 Statistical test: independent samples test ................................................. 53

3.6.5.4 Associations between Nutritional Status, Dental Maturity and Dental

Maturity ................................................................................................................... 53

3.6.5.4.1 Nutritional status and dental maturity ...................................................... 53

3.6.5.4.1.1 Spearman‘s statistical test ..................................................................... 54

3.6.5.4.2 Nutritional status and dental age .............................................................. 54

3.6.5.4.2.1 Spearman‘s statistical test ..................................................................... 54

3.6.5.5 Null Hypothesis ........................................................................................... 55

CHAPTER FOUR .................................................................................................... 56

4.0 DISCUSSION ..................................................................................................... 56

4.1 SOCIO- DEMOGRAPHICS ............................................................................. 56

4.1.1 Distribution of the caregiver‘s marital status and the person who provided

care .......................................................................................................................... 56

4.1.2 The caregiver‘s level of education and the type of job .................................. 56

4.1.3 Natal History .................................................................................................. 58

4.1.4 Feeding Habits during the infancy period ...................................................... 59

4.1.5 Distribution of the children by chronological age and gender ....................... 61

4.2 ESTIMATED DENTAL AGE .......................................................................... 61

4.2.1 The mean estimated dental age by gender ..................................................... 61

4.2.2 Estimated Dental Age Delayed and Not Delayed .......................................... 62

4.3 ORAL HYGIENE STATUS .............................................................................. 65

4.3.1 Plaque score and gingival index ..................................................................... 65

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4.3.2 Oral hygiene by age ....................................................................................... 66

4.3.3 The severity of gingivitis by age and gender ................................................. 66

4.3.3.1 Chronological age ....................................................................................... 66

4.3.3.2 Gender ......................................................................................................... 67

4.4 EARLY CHILDHOOD CARIES ..................................................................... 67

4.4.1 Severity of ECC in the study group ............................................................... 67

4.4.2 Gender and ECC ............................................................................................ 68

4.4.3 Chronological age and ECC ........................................................................... 68

4.4.4 Dental maturity and ECC ............................................................................... 69

4.4.5 Estimated dental age and ECC ....................................................................... 69

4.5 NUTRITIONAL STATUS ................................................................................ 70

4.5.1 Weight for Height Z Scores ........................................................................... 70

4.5.2 Weight for Age Z Scores................................................................................ 71

4.5.3 Height for Age Z Scores ................................................................................ 71

4.5.4 Nutritional status and ECC ............................................................................ 71

4.5.4.1 Weight for Height Z scores and severity of ECC ....................................... 72

4.5.4.2 Weight for Age Z scores and severity of ECC ............................................ 73

4.5.4.3 Height forAge Z scores and severity of ECC ............................................. 78

4.5.4.4 Statistical test .............................................................................................. 78

4.5.4.5 Null Hypothesis ........................................................................................... 78

4.5.5 Nutritional Status and Dental Age ................................................................. 78

4.5.5.1 Weight for height for age z score and dental age ........................................ 79

4.5.5.1.1 Statistical test: independent samples test ................................................. 80

4.5.5.2 Weight for age Z scores and dental age ...................................................... 81

4.5.5.2.1 Statistical test ........................................................................................... 81

4.5.5.3 Height for age z score and dental age ......................................................... 81

4.5.5.3.1 Statistical test: independent samples test ................................................. 82

4.5.5.4 Null Hypothesis ........................................................................................... 83

xii

4.6 CONCLUSION ................................................................................................... 84

4.7 RECOMMENDATIONS ................................................................................... 85

REFERENCES ......................................................................................................... 86

APPENDICES ........................................................................................................ 104

APPENDIX I: Ethical Approval Letter.................................................................... 104

APPENDIX II: NACOSTI Research Authorization Letter ..................................... 106

APPENDIX III: Nairobi City County Research Authorization Letter..................... 107

APPENDIX IV: Kenya Bureau of Standards Calibration Certificate

(BS/MET/2/3/798) ................................................................................................... 108

APPENDIX V: Kenya Bureau of Standards Calibration Certificate

(BS/MET/2/3/91/799) .............................................................................................. 110

APPENDIX VI: Schedule of Activities ................................................................... 112

APPENDIX VII: Budget .......................................................................................... 113

APPENDIX VIII: Consent Information Document (English) ................................. 116

APPENDIX IX: Consent Information Document (Swahili) .................................... 119

APPENDIX X: Statement of Consent (English) ...................................................... 121

APPENDIX XI: Statement of Consent (Swahili) .................................................... 123

APPENDIX XII: Data Collection Form .................................................................. 124

APPENDIX XIII: Caregivers' Questionnaire (English) ........................................... 127

APPENDIX XIV: Caregivers' Questionnaire (Swahili) .......................................... 131

APPENDIX XV: Demirjian's Tooth Maturity Chart ............................................... 135

APPENDIX XVI: Demirjian‘s Tooth Stage Descriptions ....................................... 136

APPENDIX XVII: Tooth Notation .......................................................................... 137

APPENDIX XVIII: Demirjian‘s Tables (Maturity Scores) ..................................... 138

APPENDIX XIX: Certificate of Originality ............................................................ 139

APPENDIX XX: Declaration of Originality............................................................ 140

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LIST OF FIGURES

Figure 1: Distribution of the children‘s caregiver and the marital status of the

caregivers. .................................................................................................................. 24

Figure 2: Distribution of the caregiver by the level of education and occupation. ... 25

Figure 3: Postnatal history of 171 children aged 3-5 years. ...................................... 26

Figure 4: Infancy feeding habits for the children involved in the study, n=171 ....... 27

Figure 5: Distribution of the children by gender and by age group .......................... 28

Figure 6: Distribution of the children by age categories ........................................... 29

Figure 7: The means for chronological age by gender, n=171 ................................. 30

Figure 8: The means for chronological age ............................................................... 31

Figure 9: The mean estimated dental age by age groups .......................................... 32

Figure 10: The mean estimated dental age by gender ............................................... 33

Figure 11: Differences between the dental age for the delayed and not delayed ...... 34

Figure 12: Oral hygiene status by age and chronological age ................................... 35

Figure 13: Oral hygiene status and severity of gingivitis.......................................... 36

Figure 14: Severity of gingivitis by age and gender ................................................. 37

Figure 15: ECC and oral hygiene status .................................................................... 37

Figure 16: The decayed, missing, filled components of the dmft ............................. 38

Figure 17: Distribution of dmft by gender and chronological age ............................ 39

Figure 18: Distribution for the weight for height for age z scores for children aged

36-59 months with WHO reference standards, n=171 ............................................... 41

Figure 19: The weight for height mean z-scores for the age group for children aged

36=59 months ............................................................................................................. 42

Figure 20: The weight for height for age mean z scores for females and males age

aged 36-59 months compared to WHO reference standards, females (n=84), males

(n=87) ......................................................................................................................... 43

Figure 21: Distribution for the weight for age z scores for children aged 36-59

months with WHO reference standards, n=171 ......................................................... 44

Figure 22: The weight for age z-scores for age groups for children aged 36-59

months ........................................................................................................................ 45

xiv

Figure 23: Weight for age mean z scores for males (n=84) and females (n=87). aged

36-59 months .............................................................................................................. 46

Figure 24: Distribution of the children aged 36-59 months by height for age z

scores .......................................................................................................................... 47

Figure 25: The height for mean age z-scores for children aged 36-59 months ......... 48

Figure 26: The height for age mean z scores for males (n=84) and females (n=87). 49

Figure 27: Distribution of the children by nutritional status Z-scores for WHZ, HAZ,

WAZ, and ECC, n=171 .............................................................................................. 51

Figure 28: The nutritional status of the children with mean dental age, n=171 ........ 52

Figure 29: The estimated dental age and the nutritional status of children, n=171 .. 55

xv

LIST OF ABBREVIATIONS

WHO World Health Organization

ECC Early Childhood Caries

S-ECC Severe Early Childhood Caries

BMI Body Mass Index

HAZ Height-for-age Z-score

WAZ Weight-for-age z-score

WHZ Weight-for-height z-score

EC-PEM Early Childhood – Protein-Energy Malnutrition

FM Body Fat Mass

DXA Dual-energy X-ray Absorptiometry

TDS Tooth Development Stages

DAA Dental Age Assessment

OPG Orthopantomogram

DMFT Decayed Missing Filled Teeth (permanent dentition)

dmft Decayed Missing Filled Teeth (deciduous dentition)

defs Decayed, Extracted, Filled, Surfaces (deciduous dentition)

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DEFINITION OF TERMS

Chronological age: The total number of years an individual has lived, used most

commonly in psychometrics as a standard against which certain variables like

behaviour and intelligence are measured.

Dental age: Dental age is the state of maturation of an individual's teeth and is

usually assessed up to 18 years of age.

Nutritional status: Nutritional status is the body‘s condition concerning the body‘s

level of nutrients and the ability of these nutrient levels to maintain healthy body

metabolic processes.

Early Childhood Decay: The presence of one or more decayed (cavitated or non-

cavitated lesions), missing (due to caries) or restored tooth surfaces in any deciduous

tooth in children aged 71 months or younger.

Underweight: Underweight is when the weight for age is less than -2 standard

deviations (SD) of the WHO Child Growth Standards median.

Overweight: Overweight is when the weight for height is greater than +2 standard

deviations (SD) of the WHO Child Growth Standards median.

Obese: Obese is when the weight for height is greater than +3 standard deviations

(SD) of the WHO Child Growth Standards median.

Wasting: Wasting is when the weight for height is less than -2 standard deviations

(SD) of the WHO Child Growth Standards median.

Severe wasting: Severe wasting is when the weight for height is less than -3

standard deviations (SD) of the WHO Child Growth Standards median.

Stunting: Stunting is when the height for age is less than -2 standard deviations (SD)

of the WHO Child Growth Standards median.

3-5-year-olds: For this study, this age category includes children aged 36-59 months.

xvii

ABSTRACT

Introduction: Dental caries is becoming an increasingly common occurrence in

children aged five years and below. Early childhood caries (ECC) if left untreated,

may negatively impact on dietary intake and the child is at risk of being underweight,

stunted or both.

Broad objective: The broad objective was to determine the relationship between

chronological age, dental age, nutritional status and early childhood caries among 3-

5-year-old children.

Study Setting: The study was carried out at the Lady Northey City County Dental

Clinic.

Study design: The survey was descriptive and cross-sectional in design and was

carried throughout five months.

Sampling and sampling technique: Purposive sampling was employed where every

child aged 3-5 years presenting with ECC, and had an orthopantomogram (OPG) as a

requirement for diagnosis were selected for the study.

Data Collection Instruments: Information on socio-demographics, oral hygiene

habits and dietary habits was collected using a semi-structured questionnaire. The

WHO caries diagnostic criteria (2005) was used for assessment of dental caries.

Nutritional status was assessed using anthropometric measurements. The

determination of dental age was done using the method by Demirjian.

Data Analysis: Analysed using SPSS version 25.0 for windows. The WHO Anthro

Statistical Programme was used in the analysis of nutrition data. Statistical tests were

performed for different variables to establish relationships between them.

Results: The mean chronological age for the 171 children who participated in the

study was 4.09±0.54, se=0.042years (range 3 - 4.92 years). The mean estimated

dental age for the children was 4.59±0.75, se=0.57. The 136 (79.5%) whose dental

age was not delayed had a mean dental age of 4.79 ± 0.62, se=0.05, (range 3.30 -

xviii

6.70 years) while 35 (20.5%) whose dental age was delayed had a mean 3.82 ± 0.73,

se=0.12, (range 2.20 -4.80 years). The differences in the mean age between those

with delayed dental age and those without delay in dental age were statistically

significant with a Levene's test for equality of variances with F=4.649, df= (169, 47),

p=0.000.

There was no significant relationship between dental age and dmft (correlation

coefficient r=0.045, p=0.563). The relationship between nutritional status and ECC

was not statistically significant using Spearman‘s correlation.

A significantly strong and positive association was noted between height for age z

score and dental age where a Spearman‘s correlation r=0.314, p=0.000 and weight

for age z score r=0.202, p=0.008 at 95% CL.

Conclusion: Most of the children‘s dental age was advanced when compared to the

chronological age. The dental age had associations with underweight and stunting.

There was no relationship between dental age and ECC. Hence the severity of early

childhood caries may not be a good indicator of delayed dental age. There was

however no relationship between nutritional status and ECC.

Recommendations: There may be need to establish a reference dental age dataset

for Kenyan children of African descent. When determining the dental age, stunting

and underweight should be taken into consideration.

1

CHAPTER ONE

1.0 INTRODUCTION AND LITERATURE REVIEW

1.1 INTRODUCTION

Dental caries is a known global public health problem. ECC is experienced in both

developed and developing countries. ECC affects a child‘s life quality with adverse

effects ranging from pain, infection, inability to feed correctly and inadequate sleep.

A child may be forced to miss school days due to pain and infection caused by ECC,

and this affects academic performance. Socialisation is also affected because of the

unsightly nature of gross caries.

An individual‘s growth is influenced by many factors including genetics, race,

nutritional status, hormones, socio-cultural environment and climatic conditions(1)

.

However, the mineralisation of teeth is less affected by these factors than other

growth parameters(2–4)

. Also, dental age shows the least variability concerning

chronologic age, when compared to other maturity indicators (2,5,6)

.

The estimation of dental age is of importance because of its application in criminal,

medical, legal and civil practices, archaeological and anthropological studies. It is

highly invaluable in orthodontics and paediatric dentistry as it affects diagnosis,

treatment planning and treatment of paediatric patients. During medico-legal

processes involving uncertain or unknown birth records, dental age supplements other

indicators of maturity in chronological age estimation (2)

. Dental age assessment

using tooth development is more reliable than using tooth eruption because tooth

emergence into the oral cavity is a brief occurrence, whereas development can be

referred to at any age (7)

.

2

1.2 LITERATURE REVIEW

1.2.1 EARLY CHILDHOOD CARIES

1.2.1.1 Definition of early childhood caries (ECC)

Early childhood caries (ECC) has been defined as when there are one or more carious

teeth (cavitated or non-cavitated lesions), missing (due to caries) or restored tooth

surfaces in any deciduous tooth in children aged 71 months or younger (8)

. Any sign

of smooth-surface caries is categorised as severe early childhood caries (S-ECC) in

children younger than three years of age (9)

. S-ECC in children aged three to five is

the presence of one or more teeth with cavities or filings on the smooth surfaces in

the deciduous maxillary anterior teeth. Also, a score involving four or more smooth

surfaces of teeth at age three, or five or more teeth with cavities or fillings at age four

and six or more than six surfaces of the primary teeth are filled or affected by dental

decay at age five years is considered to be S-ECC (9)

.

1.2.1.2 The prevalence of early childhood caries in Kenya

ECC is regarded as a severe public health problem both in developed and developing

countries. According to the Kenya National Oral Health Survey Report (2015), 5-

year-olds had a higher prevalence of dental caries (46.3 %) as compared to other age

groups (10)

.

1.2.1.3 Consequences of early childhood caries

The consequences of ECC include an increased risk of developing new carious

lesions in both the deciduous and permanent dentitions. Developing a higher

frequency of hospitalisation and emergency treatments (due to odontogenic infections

and pain), increased costs and time of treatment, inadequate or poor physical

development (more so in height/weight). There is an increase in days with restrictive

activity, loss of productive school days, decreased learning abilities and generally low

life quality (9)

.

3

1.2.2 NUTRITIONAL STATUS

1.2.2.1 Definition of nutrition and nutritional status

Nutrition is the intake of food concerning the body‘s dietary needs (11)

. A poor diet

can lead to reduced body immunity, increased susceptibility to various diseases,

mental and physical development impairment, and decreased productivity in general.

Nutritional status is the body‘s condition concerning the body's level of nutrients and

the ability of these nutrient levels to maintain healthy body metabolic processes (12)

.

1.2.2.2 Factors affecting the nutritional status of children aged 5 years and

under

A child‘s nutritional status is influenced by several factors ranging from social,

economic and environmental factors. Geographic and regional disparities in

malnutrition have been reported (13)

, with some regions in Kenya having a higher

malnutrition prevalence than others. In Kenya and Zambia, children from wealthy

households with electricity are less likely to have stunting (14)

. Gender and age also

play a role. The boys have a higher risk of being stunted than the girls (13–15)

. Younger

children are less likely to be stunted than older children in this age group (13–15)

. The

breastfeeding duration also affects nutritional status. Children who are weaned early

have an increased risk of being underweight (16)

. Immunisation status is another major

role player since up-to-date vaccinations are protective against some childhood

illnesses. A child who has suffered from an upper respiratory tract infection or any

other disease in the past month is highly likely to be underweight (16)

. Children

belonging to mothers who are better educated are less likely to have stunting (14)

. A

child living with non-biological parents has a higher likelihood of stunting (16)

.

1.2.2.3 Methods of assessing nutritional status

An individual‘s overall nutritional status is determined using anthropometry. It is

non-invasive and inexpensive. Age, sex, and length/height and weight are the

building blocks that makeup anthropometry. An index is what results when any two

4

of these variables are used together. In children, nutritional status is usually assessed

using the following three indices:

i. Weight for age

ii. Height for age (length for age for children of the age of 2 years and

below)

iii. Weight for height (weight for length for children aged two years and

below)

Malnutrition (underweight, stunting and wasting) can be characterised by weight,

height, and body mass index (BMI) deficiency or excess. BMI is expressed in kg/m2

and is used in children aged 24 months and above (17)

. BMI is classified into four

categories which correspond with specific percentile ranges in children and particular

ranges of scores in adults; underweight, healthy, overweight and obese (17)

.

Low weight for age index signifies an age-specific underweight condition, and it

indicates both present (acute) and past (chronic) undernutrition but does not

differentiate between chronic and acute conditions (18)

. Reduced height for age index

signifies chronic malnutrition (stunting) condition or a past under-nutrition, but it

cannot determine the short-term malnutrition changes (18)

. Reduced weight for height

identifies children with either acute or current under-nutrition or wasting (useful

when the precise ages are challenging to determine) and is used for deciding short-

term effects like food supply seasonal changes or short-term nutritional stress brought

about by an illness (18)

.

1.2.3 CHRONOLOGICAL AGE

Chronological age is the number of years an individual has lived, used most

commonly in psychometrics as a standard against which certain variables like

behaviour and intelligence, are measured (19)

.

5

1.2.3.1 Methods of assessing chronological age

Chronological age is evaluated by referring to birth records: birth certificates or birth

notifications. In the current world, accurate records of dates of birth exist, and this

information can be gotten from most parents and caregivers. There are instances,

however, where birth dates are not reliably documented, more so in developing

countries. In such cases correlating the physical, dental and skeletal maturity of an

individual is used to assess age. Given the fixed pattern of the eruption of deciduous

dentition in children, Towlson and Peck (20)

estimated chronological age by using the

total number of erupted teeth. This study concluded that owing to the variability in

eruption at any given chronological age, the number of erupted teeth does not

accurately estimate dental age (20)

.

The use of radiographs such as those of the hand and wrist in the assessment of

skeletal age can be useful in chronological age estimation. A high correlation exists

between dental age and skeletal age while chronological age has an inconsistent

association with both skeletal and dental ages (21,22)

.

Knowledge of chronological age is highly paramount in a child‘s nutritional status

assessment. The four measures used in anthropometric assessments are sex, age,

weight and length/height. When two of these variables are used in combination, they

are called an index. Usually, height and weight vary and increases with increase in

chronological age. In paediatric dentistry practice, chronological age determines the

course of treatment taken in managing dental caries, in behaviour modification and

management during treatment, and consideration of whether a carious tooth is to be

extracted or restored.

1.2.4 DENTAL MATURITY AND DENTAL AGE

Dental maturity is usually expressed as a dental age (2)

. Dental age is the state of

maturation of an individual's teeth and is generally assessed up to 18 years of age.

Dental age is a maturity indicator. Maturity indicators are distinct events in a series of

processes (e.g. dental, sexual, skeletal) that show an individual's uneven maturation

6

(23). Maturational processes, the relationship between maturity and size together with

sexual dimorphism are independent and are not associated with the passage of

chronological time, but to the progression of the individual to a mature state from a

state of immaturity (23)

. Thus, the chronological age of an individual does not always

tally with dental age.

Growth and development are significantly under the influence of hereditary

(genetics), nutritional, sexual, metabolic, functional, cultural, social and

environmental factors. In age assessment, teeth are preferable over skeletal methods

because of their durability in archaeological contexts, minimal remodelling and

continuous development over the entire juvenile period (1)

. Dental development is less

influenced by environmental factors when compared to skeletal growth and

development (1)

. Tooth eruption is less variable than skeletal maturation. Tooth

formation is less variable than tooth eruption (6)

. As a biological maturity indicator in

children, dental development is more reliable(1,24,25)

. When compared to other

maturational factors, dental development has been shown to correlate more closely

with chronological age in children, adolescents and young adults (3,5,6)

. From the

analysis monozygotic and dizygotic pairs of twins, it is believed that dental

development/tooth formation timing is mostly genetically determined(6)

.

There are several methods of dental age assessment. The two most commonly used

methods are the use of dates of tooth eruption and tooth calcification/mineralisation

of a single tooth or several teeth (7)

. Gingival emergence (tooth emergence) as a

method of estimating the dental age has several disadvantages. The disadvantages

may include; local factors influence such as premature loss of deciduous teeth,

crowding/insufficient space in the dental arch. The impaction of teeth or tipping;

influenced by systemic factors such as nutritional factors; limited use between ages

30 months to 6 years and beyond 13 years (except for third molars whose eruption is

highly varied and also tends to be absent in 29% of the population) (7,26)

. The eruption

also depends on the timing of observation, given that it is a discontinuous process (7)

.

Tooth emergence (gingival emergence) is usually incorrectly termed as tooth

7

eruption. Tooth eruption is a continuous process. Tooth emergence is a brief event in

this process and therefore the chance that the time of observation may coincide with

the specific gingival emergence timing is very small (27)

.

The use of developing teeth, which is least influenced by environmental and systemic

factors, is thus a more reliable method of estimating dental age (6,26)

.

1.2.4.1 The need for dental age estimation

Sub-Saharan Africa records the lowest registration of births levels (44%), with

Eastern and Southern Africa having about 44 million children aged under five

unrecorded (28)

. Dental age estimation is therefore widely used to estimate

chronological age, for both medical and legal purposes, in children without birth

records (1)

.

Dental age estimation on panoramic radiographs is useful for orthodontists and

paediatric dentists in choosing an appropriate treatment plan. It is used as an essential

indicator in identifying abnormal development and eruption sequences so that

intervention to prevent dental decay is put in place (1)

.

Estimation of age is an essential requirement in legal, judicial and criminal court

proceedings (29)

. Other circumstances where age assessment is a requirement are;

asylum seekers whose chronological ages are not known, young people accused of

the crime, and convicted criminals who are claimed to be minors (less than 18 years)

before sentencing (30)

. According to universal laws, any asylum seeker aged below 18

years should be considered a minor and reserves the right of abode in the country

where asylum is claimed. Age assessment is sometimes required to help in the

process of identification of subjects from mass disasters (31)

and murder victims.

1.2.4.2 Imaging and dental age estimation

Dental and maxillofacial tissues imaging play a significant role in dental age

assessment. Different assessment methods use different types of radiographs. There

are several methods of estimating age. Among these, the radiographic approach is

preferred since it is a reproducible, non-invasive and straightforward method (31,32)

.

8

These include intraoral radiographs, lateral oblique views of the mandible, panoramic

radiographs and cone beam computed tomography (CBCT). Panoramic radiographs

are widely used since they provide images of both maxillary and mandibular

dentition. The calcification status can now be determined with precision due to

technical advancements in digital radiography and CBCT (1).

1.2.4.3 Classification of dental age estimation methods

These can be classified broadly into two; the technique used and the subject‘s age.

Based on the method of age estimation, there are four categories; histological

method, chemical and physical analysis, visual method and the radiographic method.

Among these, the radiographic technique is the most widely used. Based on the study

subject‘s age, there are also four categories; age estimation in the prenatal period, in

infants, in children and adolescents and adults (1,33)

. Dental age estimation methods

are used during the prenatal and postnatal period since tooth development can be

observed from the sixth week of intrauterine life.

1.2.4.4 Estimation of dental age in children and adolescents.

In this group, dental age is estimated based on either eruption or calcification of teeth

(32). Several studies have confirmed that tooth development is by far a more reliable

dental maturity indicator as compared to gingival emergence into the oral cavity

(26,34,35).

Dental age estimations are based on the measurement of the pulp-tooth ratio, open

tooth apices, and tooth development staging. There are various radiographic methods

of assessing dental age which use developing teeth and these include:

i. The Demirjian Method (34,36)

ii. The Haavikko Method (37)

iii. Willem‘s Method (26)

iv. Nolla‘s Method (38)

v. Cameriere‘s method (39)

vi. Moores, Fanning and Hunt method (40)

9

vii. Kvaal's method (41)

Most methods use a few selected teeth. However, some methods use all permanent

teeth (Nolla, Haavikko). The study of all the teeth is not only expensive and time-

consuming but also presents technical challenges (7)

.

1.2.4.5 The Demirjian Method of Dental Age Estimation

This method was proposed in 1973 (34)

and has emerged as the most widely used and

researched method of estimating dental age in children and adolescents (7,32,42–44).

Demirjian method has been adopted because of its simplicity and the schematic and

radiographic illustrations of tooth development which are accompanied by a

description (42)

.

It was based on a system for the hand and wrist maturity estimation developed by

Tanner et al. (32)

. This method uses dental maturity scores (Demirjian‘s tables) and

percentile curves derived from the evaluation of the OPGs of 4,756 French-Canadian

children aged between 2-20 years (36)

. These scores serve as a reference dataset used

in chronological age evaluation for different population groups (29)

.

The Panoramic radiographs used in Demirjian's method are more comfortable to take

as compared to intra-oral radiographs more so in children. They also give less

radiation and capture all teeth. Also, the left mandibular region which is the area of

interest in this method undergoes minimal distortion in a panoramic radiograph (7)

.

The Demirjian system of maturity determination is based on the shape and not the

total length of developing dentition. Thus the 5-10% enlargement that may affect the

left side of the radiograph is inconsequential (7)

.

The Demirjian classification of Tooth Development Stages (TDS) is a system that

recognises eight tooth development stages beginning from initial calcification (Stage

A) to complete root formation (Stage H). The simplicity and reliability of this method

are due to the high inter- and interobserver agreement values (45)

.

10

A study comparing Demirjian and Cameriere‘s methods concluded that the latter

showed a higher accuracy level in all age groups, while the former showed more

relevant results for the German population that was under investigation (46)

.

Demirjian's method is accurate but has limitations when used in persons of eighteen

years and above (26)

.

Several studies done in various population groups using this method concluded that it

generally overestimated age; southern Chinese population (0.36 years in girls and

0.62 years in boys) (29)

, Norwegian children (0.3 years in girls and 0.2 years in boys)

(2), a meta-analysis of 26 published studies (0.39 years in girls and 0.35 years in boys)

(47), Nigerian children

(48), Iranian children aged 3.5-13.5 years (0.21 years in girls and

0.15 years in boys) (42)

, 6-13 year-old Iranian children (0.25 years in girls and 0.34

years in boys) (43)

, eastern Turkish children (0.2-1.9 years in girls and 0.4-1.3 years

in boys) (49)

, western Turkish children (0.28-0.87 years in girls and 0.10-0.7years in

boys) (50)

, Venezuelan children (0.62+/- 0.93 years) (51)

, Indian children (0.04 years in

girls and 0.14 years in boys) (52)

, Norwegian children (0-7.5 months in girls and 1.5-

4.0 months in boys) (2)

, Chinese children (0.0071-1.25 years in girls and –1.0 to 1.3

years in boys) (44)

, Serbian children (0.42 years in girls and 0.45 months in boys) (53)

and Belgian Caucasian (0.7 years in girls and 0.4 years in boys) (26)

.

Demirjian et al. in their original publication (34)

stated that the standards they had

derived from the French-Canadian sample may not be applicable in other populations

and that adaptations should be obtained for other population samples.

Following the Demirjian‘s method, there are different data sets for dental age

estimation (adapted scoring systems) that have consequently been set up for different

ethnic groups. These new data sets were developed following several global studies

that demonstrated the inappropriateness of applying the French-Canadian data set

used by Demirjian to various ethnic groups. Some of the new data sets include those

of the southern Chinese population (54)

, Belgian Caucasian (26)

, South Indian (55)

,

Finnish (56)

and the Afro-Trinidadian dataset and the UK dataset for Caucasians (57)

. A

universal dataset for dental age estimation has been set up by the Dental Age

11

Research London Information Group (DARLInG) for the facilitation of the

development of these reference datasets.

The adapted scoring systems based on the original method by Demirjian have been

validated and been shown to give more precise population estimations in the specific

populations for which they were adapted (26)

. However, just like Demirjian‘s maturity

scores, these modified scores may not be valid when used in other populations (26)

.

Some studies seem to approve the applicability of Demirjian's method, especially for

the Swedish population (2,46,58–60)

. A dataset prepared either from a similar or a

different population group can be used in dental age estimation (29)

.

Despite the overwhelming evidence that this method overestimates dental age, it is

the method which has been selected for this study. The selection was because it is the

most widely used, therefore, it is possible to compare data on maturity scores of the

sample population to those of other numerous communities all over the world that

have used the same method. In literature, not much information is known about the

applicability of the Demirjian method in the Kenyan population and the African

population in general. Should there be an overestimation of age, subsequent studies

on the Kenyan population can help come up with a specific data set of adopted

maturity scores from the Demirjian method, which has been done for other

communities to improve on precision and validity.

1.2.5 THE RELATIONSHIP BETWEEN CHRONOLOGICAL AGE, DENTAL

AGE, NUTRITIONAL STATUS AND EARLY CHILDHOOD CARIES

1.2.5.1 Early childhood caries and nutritional status

Several studies have tried to determine the relationship between BMI and ECC/S-

ECC. The results are varied and conflicting— some associate S-ECC with

underweight and failure to thrive, suggesting that S-ECC may lead to a low BMI (17)

.

The significant association between ECC and overweight is associated with the

shared risk factors between the two variables (high sugar diet) according to some

12

studies. Others have not found any significant association between ECC and nutrition

(17).

A study by Alvarez et al. reported that peak caries activity is significantly higher in

wasted children and in those who were both stunted and wasted when compared with

healthy controls. The study concluded that malnutrition resulted in increased caries

experience in the deciduous teeth, affected the dental caries age distribution and

delayed tooth development (61)

.

A longitudinal study reported that children who were both stunted and wasted in

infancy had by the age of four years recovered from the malnutrition. The beginning

of their curve had shown a significantly higher caries experience in the deciduous

teeth than those who had healthy growth (62)

. Children with low weight for age and

those from families of low-income have a higher risk of dental caries experience,

compared to those with a healthy weight and from good socio-economic backgrounds

(63). A child with S-ECC is 1.23 times more likely to become underweight when

compared to his/her caries free peers (64)

.

A higher dmft-DMFT index has been found in obese study subjects in comparison to

healthy controls (65)

. A significant association has been observed between high BMI

and dental caries in both permanent and primary dentition (66)

. BMI z-scores that are

significantly higher has been recorded in children with S-ECC when compared to

their caries free peers (17)

.

Insignificant relationships between increasing dmft and the deficiency in BMI,

weight and height have been reported in other studies (67)

. A study involving children

with S-ECC found that BMI percentile was not correlated with number of teeth with

pulpal involvement or with dmft. A survey by Costacurta et al., yielded conflicting

findings: with the BMI classification (underweight, healthy weight, pre-obese and

obese), the association between increase of dmft-DMFT and pre-obesity/obesity was

not significant, but with FM% classification (Body Fat Mass Percentage - WHO cut-

offs), the pre-obese/obese subjects had higher caries indexes than their regular weight

counterparts, both in deciduous and permanent teeth. Besides, with the FM%

13

(McCarthy cut-offs) classification, a higher caries index was found in obese children

compared to pre-obese and normal-weight children both in deciduous and permanent

teeth, but their dmft-DMFT value was comparable with that of underweight children

(65).

BMI misclassifies the status of adiposity of children when compared to DXA (Dual-

energy X-ray Absorptiometry), which provides a more specific assessment of body

composition and careful screening. The conflicting data in the literature between

dental caries and obesity could be explained by the misclassification of childhood

obesity (determined by the BMI) (65)

.

Diet is essential in the development of dental caries. High refined foods and snacks

consumption, primarily those rich in sucrose, is associated with a high dental caries

prevalence (68)

.

1.2.5.2 Early childhood caries and dental age

It is a well-known fact that ECC can result in the premature loss of primary teeth and

this can have many consequences. A lack of literature exists on the relationship

between dental maturity and dental caries. Most studies available focus on the

premature loss of primary molars and the eruption of their permanent successors. A

survey by Fanning showed that the rate of formation of premolars was not affected

following the extraction of the deciduous precursor (69)

.

Regarding the effect of premature loss of primary molars, there are varied and

sometimes contradicting findings in the literature. It seems the variations are

dependent upon the timing of the extractions. If the removal is done before complete

premolar crown formation (five years and below), then the premolar tooth eruption is

delayed (69,70)

. If the extraction is done at a later period when the premolar has

advanced root formation (between 8-10 years), then an eruption is greatly accelerated

(69,70).

Retardation in eruptive movement and gingival emergence of a tooth following the

extraction of its predecessor could result from scar tissue formation which

14

mechanically resists eruptive changes (69,71)

. Acceleration of a permanent successor

tooth eruption can occur if the deciduous successor is infected with abscess

formation. The dental alveolar infections have been attributed to bone destruction

which accompanies long-standing odontogenic infections. Bone destruction creates a

path of minimal resistance for the erupting successor which may sometimes erupt

with an immaturely developed root (69,71)

.

When the predecessor is not extracted, successor tooth eruption can still be affected.

Gingival emergence of premolars (both maxillary and mandibular) is accelerated by

2-8 months when the predecessors had dental caries or had been restored but had not

been extracted (72)

.

1.2.5.3 Nutritional status and dental age

Healthy growth in children can only occur when there is an adequate and appropriate

dietary intake of all the required nutrients. Proper nutrition is necessary for body

function, healing, energy expenditure and metabolic stress. Nutrients essential for

tooth development are phosphorus, calcium, vitamins A, C and D. A small amount of

fluoride incorporation during development makes the tooth resistant to dental caries.

A deficiency in nutrients could result in dental and bone (hard tissues) hypoplasia, or

hypomineralisation or both.

Malnutrition affects tooth development. Protein-energy malnutrition is the most

pressing in Kenya, with infants, pre-school and school children being affected the

most (13)

. Dental age is retarded in underweight children, those with protein-energy

malnutrition (73)

and anaemic children (74)

. Delayed eruption and exfoliation of

deciduous teeth (61,62,75)

and delayed eruption of permanent teeth (75)

has been

observed in underweight children. Obesity has been associated with advanced tooth

formation (6,76,77)

.

1.2.5.4 Chronological age and dental age

Dentition development varies among different populations, and therefore,

chronological age cannot be used to investigate maturity with precision (78)

. The

15

development of teeth correlates more closely with chronological age in young adults,

adolescents and children, when compared to other factors of maturation (3,5,6)

.

1.3 STATEMENT OF THE PROBLEM

The prevalence of dental caries is on the rise. The Kenya National Oral Health

Survey Report (2015) placed dental caries prevalence among children aged five years

at (46.3 %), which was higher than in other age groups (10)

. The pain resulting from

untreated dental caries may interfere with the child‘s nutrition, sleep,

learning/schooling, development and growth, among others. Dental caries can result

in the premature loss of teeth which has several effects on dental age, occlusion, and

can induce the development of psychological problems, and difficulty in eating.

Only 38 % of children are registered by the age of five years in Eastern and Southern

Africa, leaving approximately 44 million children (5 years of age and below)

unregistered (28)

and therefore without documents to prove their actual age. In such

cases use of dental age and other methods to estimate chronological age becomes

very vital. In Kenya today, no standardised method of dental age assessment is in

place. Most dentists use eruption age/ eruption sequence to estimate age, yet this is

highly variable and unreliable compared to tooth formation.

1.4 JUSTIFICATION OF THE STUDY

Early childhood caries can affect both dental age and nutritional status. There is

inadequate information on the relationship between dental age and nutritional status

among children with ECC. The methods used to assess dental age generally do not

factor in the effect of ECC and nutrition on dental age. The survey aimed to establish

information on the relationship between these variables and to try and quantify to

what extent nutrition and ECC affect dental age, which may influence policy in the

assessment of dental age for varying uses. The study also assessed the validity of

using the Demirjian dental age estimation method in Kenyans of the African race as

information on this is scarce. The findings from this study shall contribute to

16

scientific knowledge as currently there is a scarcity of information on ECC,

chronological age, dental age and nutritional status.

1.5 STUDY OBJECTIVES

1.5.1 Main Objective

To determine the relationship between dental age, chronological age, nutritional

status and early childhood caries in children aged 3-5 years.

1.5.2 Specific objectives

1. To determine the relationship between ECC and dental age.

2. To determine the relationship between ECC and nutritional status.

3. To determine the relationship between nutritional status and dental age.

4. To compare chronological age and dental age in 3-5-year-old children with

ECC.

1.6 HYPOTHESES

1.6.1 Null hypotheses

1. There is no relationship between early childhood caries and dental age in 3-5-

years-olds in Nairobi, Kenya.

2. There is no association between early childhood caries and nutritional status in 3-

5-years-olds in Nairobi, Kenya.

3.There is no association between the dental age and nutritional status in 3-5-years-

olds in Nairobi, Kenya.

4. There is no relationship between chronological age and dental age in 3-5 years-

olds in Nairobi, Kenya.

17

1.7 VARIABLES

1.7.1 Independent variables

i. Chronological age

ii. Gender

1.7.2 Dependent variables

i. Dental age

ii. Decayed, missing, filled teeth

iii. Weight for age

iv. Weight for height

v. Height for age

1.7.3 Sociodemographic variables

i. Care-giver‘s level of education

ii. Care-giver‘s occupation

iii. Care-giver‘s marital status

iv. Number of siblings

v. Birth order

1.7.4 Confounding variables

i. Oral hygiene status

ii. Dietary practices

iii. Gender

18

CHAPTER TWO

2.0 MATERIALS AND METHODS

2.1 STUDY AREA AND POPULATION

2.1.1 Study area

The research was carried out in Nairobi County, Kenya which is the capital city of

Kenya with an estimated population of 2,750,547 (81)

.

The study was explicitly carried out at the Lady Northey Hospital, which is located

along Statehouse Road, Nairobi. This hospital sees 153 patients (averagely) of the

age of 5 years or younger each month.

2.1.2 Study population

The study population included children aged 3-5 years attending dental clinics at the

Lady Northey Hospital.

2.1.2.1 Inclusion criteria

i. Children aged 3-5 years. Age was determined in full months (36-59 months).

ii. Children with ECC.

iii. Children of the African race in the Kenyan population.

iv. Children whose caregivers had to give consent.

v. Children who gave assent.

vi. Children with OPGs taken as a requirement for diagnosis.

vii. Availability of complete patient records (date of birth, date of the radiograph).

viii. Good quality of radiographs.

2.1.2.2 Exclusion criteria

i. Children with impacted teeth or localised oral pathologies/anomalies that

would affect dental development.

ii. Children with systemic syndromes or diseases (congenital anomalies)

affecting skeletal and dental development.

19

iii. Children without ECC.

iv. Children with present or history of orthodontic treatment.

v. Children with severe malocclusion.

vi. Aplasia of at least two corresponding teeth on both sides of the mandible.

2.2 METHODOLOGY

2.2.1 Study design

The study was a descriptive cross-sectional and carried out in five months.

2.2.2 Sampling technique

A selection of children with ECC from Lady Northey Hospital was made. Purposive

sampling was employed where every child aged 3-5 years were examined. Only those

presenting with ECC were selected for the study until the required sample size was

obtained.

2.2.3. Determination of the sample size

Fishers formula was used to calculate the sample size: n = z2P(1- P) ; where C

2 =n

= 1.962 × 0.463(1 – 0.463)/ 0.05

2 ‘ and z = z value‘ P = Estimated prevalence of

dental caries among 5-year-olds according to the Kenya National Oral Health Survey

Report (2015) (46.3 %) (10)

. Since the prevalence of dental caries from the same

hospital is high (95.5%), this suppressed the sample size. Therefore, there was a loss

of statistical power. C = 1 – confidence (1 – 0.463) and n= 382.

Data obtained from Lady Northey indicates that an average of 153 patients below the

age of 5 years attended the clinic per month. Given that data was to be collected

throughout two months the sampling frame for the study was 306. Since the sampling

frame is less than 10,000, the finite population correction was used to estimate the

correct sample size, hence; nf=n/ 1+n/N; where, nf = the desired sample, 382 hence

382/ 1+/306= the calculated sample size of 170.24 and an additional 10% to account

for attrition:17.024; therefore nf=187.26≈187or rounded off to 190; hence nf=190.

20

2.3 DATA COLLECTION AND ANALYSIS

2.3.1 Data collection instruments

Information on the sociodemographic background and dietary habits of all the

children who meet the inclusion criteria was collected using a semi-structured

questionnaire.

2.3.2 Chronological age

Each patient‘s chronological age was calculated by subtracting the date of birth from

the date when the radiograph (OPG) was taken.

2.3.3 Dental caries experience

Dental mirrors and WHO (11.5) dental probes were used in conducting oral

examinations under natural light. The child sat on a dental chair, facing natural light.

Before dental caries diagnosis, teeth were dried using a piece of gauze. The World

Health Organization (WHO) 2005 caries diagnosis criteria was used. Dental caries

was diagnosed when there was a clinically detectable loss of tooth substance and

when such loss has been treated with fillings or extraction. Dmft index was used to

determine the dental caries prevalence.

2.3.4 Oral hygiene status

The oral hygiene status of the child was established based on the Loe and Silness

index for gingival health and the Silness and Loe index for plaque score.

2.3.5 Nutritional status

Anthropometric measurements were used in the nutritional status assessment. With

the children erect and barefoot, their height was measured using a standard height

board to the closest 0.5cm, and weight was measured using a Salter scale to the

nearest 0.1 kg. The WHO child growth standard reference was used to evaluate

nutritional status using the indices of WAZ, HAZ and WHZ. Any child below +/-2SD

was classified as malnourished.

21

2.3.6 Dental age

All Orthopantomograms (OPGs) included in the study were done at the University of

Nairobi Dental Hospital, Radiology Department. The OPG machine model which

was used was the Kodak 9000c. The OPG images were processed digitally.

Dental age was assessed by determining the degree of maturation of seven left

mandibular teeth (excluding the third molar). Each tooth was assigned a rating from

―A‖ to ―H‖ (Appendix XV). A to H are the eight stages of tooth development. Next,

the developmental stages were converted to maturity scores by using published

gender-specific conversion tables (Demirjian‘s maturity scores- Appendix XVIII ).

The scores for each tooth were then added to arrive at a total maturity score which

was converted to dental age using separate standard tables or graphs given for each

gender. According to Demirjian, the tooth development stages the stages are labelled

0 for no calcification and A-H for the eight calcification stages (Appendix XVI) (34)

.

2.4 DATA VALIDITY AND RELIABILITY

2.4.1 Pretesting of data collection tool

A pre-test of caregivers‘ questionnaire was done in the actual field situation in the

presence of all research team members after they had been recruited.

2.4.2 Calibration of the principal investigator

A paediatric dentist, nutritionist and a radiologist calibrated the principal investigator.

Every tenth child was clinically re-examined and every 10th

radiograph re-examined

to determine inter-examiner and intra-examiner reproducibility. For all patients, a

regular examination and measurement procedure was employed. The scores of

interest for calibration analysis were dmft, gingival index, plaque score, weight,

height and dental age.

Cohen‘s Kappa index was used to calculate reliability. The results of the calibration

were 1.00, 1.00, 0.85, 1.00, 1.00, 0.85 for intra-examiner and 1.00, 0.85, 0.80, 1.00,

22

0.85, 0.85 for inter-examiner values in relation to dmft, gingival index, plaque score,

weight, height and dental age.

2.5 DATA ANALYSIS AND PRESENTATION

Data were coded, entered into a computer using SPSS program version 22 for

windows. Data cleaning was done before analysis. Both univariate and multivariate

analysis was done. Nutrition data was analysed using the WHO Anthro program. Bar

graphs and line graphs have been used in the presentation of data.

2.6 MINIMISATION OF BIAS AND ERRORS

All the instruments were calibrated. The investigator carried out all the examinations.

Trained assistants recorded the findings in the recording schedule. The study was

restricted only to those who met the inclusion criteria.

2.7 ETHICAL CONSIDERATIONS

The research was authorised by the Research, Ethics and Standards Committee of the

University of Nairobi and Kenyatta National Hospital, Kenya before the research was

conducted. Permission to carry out research in Nairobi County was obtained from the

National Commission for Science, Technology and Innovation (NACOSTI). Also,

permission to conduct the study at the Lady Northey Dental Clinic was sought and

obtained from the Nairobi City County Health Services.

Caregivers who had taken part in the study were told the purpose of the study, before

signing a written informed consent. The children and their caregivers had the right to

withdraw from the study at any given time without suffering any consequences. All

children found to be either malnourished or with ECC were referred to relevant

clinics for further management. The confidentiality of study participants and the

protection of their identity was strictly observed. Reports from this study do not

identify any participant. The data was stored safely, and only the investigator is the

custodian.

23

2.8 STUDY LIMITATIONS

The date of birth of the study participants was not ascertained using birth certificates;

instead, this information was sought from the primary caregiver who was the mother

in the majority of the cases. However, any errors arising from this applies to all the

participants.

There are many confounding factors influencing nutritional status, dental caries and

dental age. Not all confounders, like genetics, were accounted for in this study.

The low birth weight recorded in children with ECC is more likely if they have

pulpally involved teeth. This study did not distinguish children with pulpally

involved teeth and those without, which may have affected the results of the study.

24

CHAPTER THREE

3.0 RESULTS

3.1 SOCIO-DEMOGRAPHICS

3.1.1 Distribution of the caregiver’s marital status and the person who provided

care: For one hundred and thirty-two (82%) children the caregiver of the child was

the mother while 28 (16.10%) children the caregiver was the father, two (1.20%) had

an aunty as the caregiver, and one (0.60% ) child had the grandmother as the

caregiver. One hundred and twenty-nine (75.9%) children lived with both parents

while forty-one (24.1%) lived with single parents or caregivers., Figure 1. The mean

number of people per household with whom the children lived with was four.

Figure 1: Distribution of the children’s caregiver and the marital status of the

caregivers.

25

3.1.2 The caregiver’s level of education and the type of job:

There was one (0.60%) caregiver who had no formal training, twenty eighty

(16.40%) had a primary school level of education, 75 (43.90%) had secondary school

education 53 (31.00%) technical college level of education and 14 (8.20%) had a

university level of education. Figure 2. The caregiver‘s job was described as

professional 27 (16.0 %), those with clerical and white-collar jobs were 18(10.70%);

those who were businessman/woman were 74(43.80%); those who had skilled

manual jobs were 17 (10.10%); while unskilled workers were 6 (3.60%) and those

who were unemployed. Figure 2.

Figure 2: Distribution of the caregiver by the level of education and occupation.

26

3.1.3 Natal History

There was a total of hundred and seventy-one children aged between 3-5 of whom 3-

4 were 71 (41.1%), and 100 (59.1%) were aged 4-5 years. The gestation period for

both age groups was 36.7 weeks and the mean birth weight for those aged 3-4 years

was 3.3 kg while those in the age category of 4-5 years the mean birth weight was 3.2

kg. The immediate postnatal history of the children was that 137(80.1%) of children

had had had standard delivery while 34 (19.9%) had been born through caesarian

section. All the one hundred and seventy-one children had been immunised, sixty-two

(36.9%) had suffered from different illnesses a few days before recruitment into the

study. The sickness was diarrhoea affected two (4.5%) children, and cough/cold

affected 42 (95.5%), and none of the children had recently suffered from malaria.

Thirty-one children had suffered from a chronic illness while 137 (81.5%) did not

suffer from chronic diseases Figure, 3.

Figure 3: Postnatal history of 171 children aged 3-5 years.

For the children aged 3-4 years twenty-eight (39.4%) out of 71 had traumatic dental

injuries while in the age category of the 4-5-year olds 33 (33%) out of 100

27

individuals had had traumatic injuries. In the 3-4 year age group the trauma occurred

at a mean age of 24.1 months while in the 4-5 months age group the trauma occurred

at 20.8 months post uterine, Figure 4. 0ut of the 171 children (16.4%) had suffered

from illnesses. From the 3-4-year age group out of the 28, there were 11(39.3%) who

had suffered from illness for a period 11.5±14.5 months while in the 4-5-year olds 17

(60.7%) had suffered from diseases for 12.4±14.8 months.

3.1.4 Feeding Habits during the infancy period

One hundred and forty-one (82.50%) children aged 3-5 years had exclusive

breastfeeding in their early infancy. Twenty-seven (15.80%) had breastfeeding and

bottle-feeding, and three (1.8%) had exclusive bottle feeding. There were 163 (95.90

%) children who breastfed on demand while seven (4.10 %) were not. The duration

of bottle feeding was eighteen months for those aged 3-4 years and twenty months

those aged 4-5 years. The duration of breastfeeding in both the groups of 3-4 and 4-5-

year-olds was twenty-one and twenty-two months respectively, Figure, 4.

Figure 4: Infancy feeding habits for the children involved in the study, n=171

28

3.1.5 Distribution of the children by gender and age categories

In total 208 children were recruited into the study, out of whom 37 (17.7%) were

excluded as they dropped out as they were not interested in taking the

orthopantograms they wanted treatment for their children without radiographs. The

remaining 171 children whose parents had returned the orthopantograms had their

data for anthropometrics, ECC, recorded. There were 84 (49.1%) males while the

females were 87 (50.9%).When children were categorised by age groups, where there

were 38 (45.2%) boys aged 3-3.9years while the girls in the same age category 33

(38%). For the age category of 4-4.9, there were 46 (54.8%) boys while the girls were

54 (62.1%), Figure 5.

Figure 5: Distribution of the children by gender and by age group

29

When the children were categorised into age groups, the 3-3.9 year-olds were 71

(41.5%), and those in the 4-4.9 age category were 100 (58.5%), Figure 6.

Figure 6: Distribution of the children by age categories

3.2 CHRONOLOGICAL AGE

3.2.1 Chronological age by gender

The mean chronological age for boys aged 3-3.9 years was 3.53±0.29years, se=0.05,

(range 2.92-3.92 years) while the mean age for the girls in the same age group was

3.55±0.3 years, se=0.05 (range 3.00-3.92 years). For the age, category 4-4.9 years the

mean age for the boys was 4.45±0.26, se=0.04, (range 4-4.92 years) and the girls had

a mean chronological age of4.50±0.28years, se=.04 years (range 4.00-4.92 years)

Figure 7.

The differences in the mean ages by gender was insignificant with an independent

samples test, with Levene‘s F=0.043, df= (169, 169), p=0.179.

The mean chronological age for boys aged 3-4.9 years was 3.53±0.29, se=0.05 (

range 3-3.92 years) while the girls had a mean age of 3.55±0.3 years, se=0.05 ( range

30

3-3.92 years). The boys in the age category of 4-4.9 years had a mean chronological

age of 4.45±0.26, se=0.04 ( range 4-4.92 year) while the girls in the same age group

had a mean chronological age of 4.5±0.28, se=0.04 ( range 4-4.92years). The

differences in the mean chronological age by gender and age category was

insignificant when analysed with an independent samples test where a Levene's test

for equality of variances had F= .019, df=169, p=0.190 at 95% CL.

Figure 7: The means for chronological age by gender, n=171

3.2.2 Chronological ages by age groups

The mean age for 171 children was 4.09±0.54, se=0.042years (range 3-4.92 years).

The children aged 3-3.9 years were 71 (41.5%) and had a mean chronological of

3.59±0.29 (58.5%). Children aged 4-4.9 years were 100 (58.5%) and had a mean age

4.48±.27, se=.03 ( range 4.00-4.92 years) Figure 8. However the differences in the

mean age by age group was significant with an independent samples test, with

Levene‘s equal variance assumed F=0.362, df= ( 169, 149), p=0.000 at 95% CL The

mean differences in chronological age by age category were significant with an

31

independent samples test where Levene's test for equality of variances had F=0.528,

df= (169, 144), p=0.000 at 95% CL.

Figure 8: The means for chronological age

3.3 ESTIMATED DENTAL AGE

3.3.1 The mean estimated age by age group

The mean estimated dental age for the 171 children was 4.59±0.75, se=0.57, ( range

2.20-6.7 years); The mean estimated dental age for children aged 3-3.9 was

4.13±0.77 years, se=0.09, (range 2.2-5.7 years) and those in the age category of 4-4.9

years had a mean estimated dental age of 4.92±0.54, se=0.05, (range 3.4- 6.7 years),

Figure, 9. The differences in the dental age by age category were found to be

significant with an independent samples test where a Levene‘s where equal variance

was assumed F=21.438df=(169, 117), p=0.000 at 95% CL

32

Figure 9: The mean estimated dental age by age groups

3.3.2 The mean estimated dental age by gender

Eighty-four (49.1%) boys aged 3-4.9 had a mean estimated dental age of 4.68±0.72

years, se= 0.08, (range 2.20 to 5.90 years); while 87 (50.9%) girls had a mean dental

age of 4.50±0.77, se=0.08, (range 2.6- 6.70) Figure 10 . The mean estimated dental

age for boys aged 3-3.9 was 4.31±0.84, se=0.14, ( range 2.2-5.7 years ) while the

mean estimated dental age fro the girls was 3.92±0.64, se=0.11 ( range 2.6-5 years).

In the age category of 4-4.9 years, the boys had a mean estimated dental age of

4.99±0.41, se=0.06( range 3.8- 5.9 years) while the girls in the same age category had

a mean estimated dental age of 4.85±0.62, se=0.08 ( range 3.4- 6.7 years ), Figure 10.

33

Figure 10: The mean estimated dental age by gender

The differences in the dental age for girls and boys was not significant when an

independent samples test the Levene‘s with equal variance not assumed was

insignificant with F= 0.108, df=169, 168), p=0.102.

3.3.3 Estimated Dental Age Delayed and Not Delayed

The mean dental age for the 171 children was 4.59±0.75, se=0.06 (range 3.3-6.7

years) out of whom 136 (79.5%) children had a mean dental age which was not

delayed, and the mean age was 4.79± 0.62, se=0.05, (range 3.30 -6.70 years).

However, those whose dental age was delayed had a mean dental age of 3.82 ± 0.73,

se=0.12, (range 2.20 -4.80 years), Figure 11.

The differences in the mean age between those with a delayed dental age and those

without delay in dental age were statistically significant with independent samples

Test with a Levene's test for equality of variances with F=4.649, df= (169, 47),

p=0.000.

34

Figure 11: Differences between the dental age for the delayed and not delayed

3.4 ORAL HYGIENE STATUS

3.4.1 Plaque score and gingival index

One hundred and sixty-one out of 171 children in the study were examined for plaque

scores and gingival bleeding ten children the index teeth were missing. The mean

plaque score was 2.1±0.7; while the mean gingival index was 1.1±0.6, se=0.1. The

mean plaque score for 71 (41.5%) children aged 3-4 years was 2.1±0.7, se=0.1, and

the gingival index was 1.01±0.6, se=0.1 while100 (58.5%) children aged 4-5years

had a mean plaque score of 2.0±0.6, se=0.1 and a mean gingival index of 2.1±0.6,

Figure 12.

35

Figure 12: Oral hygiene status by age and chronological age

3.4.2 Oral hygiene status by age

12 individuals were missing index teeth and were excluded from this assessment. A

total of 159 individuals aged between 3-5 were assessed for oral hygiene, where 12

(7.5%) children had good oral hygiene, sixty-one (38.4%) had fair oral hygiene and

eighty-six (54.1%) had poor oral hygiene. The severity of gingivitis was that

85(52.8%) had mild gingivitis; 74 (46.0%) had moderate gingivitis, and two (1.2%)

had severe gingivitis, Figure 13.

36

Figure 13: Oral hygiene status and severity of gingivitis

Oral hygiene status was based on plaque scores (PS); excellent (no plaque), good (PS

of 0.1-0.9), fair (PS of 1.0-1.9) and poor (PS of 2.0-3.0). The severity of gingivitis

was assessed based on gingival index (GI) as follows: mild (GI of 0.1-1.0), moderate

(GI of 1.1-2.0) and severe(GI of 2.1-3.0).

3.4.3 The severity of gingivitis by age and gender

3.4.3.1 Age: The 3-3.9-year-olds had 71 (41.5%)children out of whom 37 (52.1%)

had mild gingivitis, thirty-four (47.8%) moderate gingivitis however there were no

children in this age category that had severe gingivitis. The 4-4.9 age group had 90

(52.%) children out of whom 48(53.4%) had mild gingivitis, there were 40 (44.4%)

with moderate gingivitis, and two (2.2%) had severe gingivitis, Figure 14.

3.4.3.2 Gender: There 78(%) boys in whom the distribution of the severity of-of

gingivitis was mild 36 (46.1); moderate 41 (52.6%) and severe gingivitis affected one

(1.3%) boy. The girls were 83 (48.5%), out of whom were 49 (59%)were affected

with mild gingivitis, 33 (39.8%) had moderate gingivitis, and one (1,2%) had severe

gingivitis Figure 14.

37

Figure 14: Severity of gingivitis by age and gender

3.4.4 Oral hygiene and ECC

The children who had good oral hygiene had a mean dmft of 20.2, while those with a

fair OH had a mean dmft of 20.05 while those with poor oral hygiene had a dmft of

19.94, Figure 15.

Figure 15: ECC and oral hygiene status

38

3.5 EARLY CHILDHOOD CARIES

3.5.1 Severity of ECC in the study group

The mean dmft for the 171 individuals was 11.03 ± 4.62, se=0.35 (range 1-20

decayed teeth). The decayed component of the dmft was 10.81±4.43, se=0.34, (range

between 1to 20 decayed teeth), missing part 0.22±0.78, se=0.34 (range (0-7 missing

teeth), filled component of dmft was 0.01±0.08 se=0.01, (range 0-1 filled teeth). The

decayed component formed 98% of the dmft while the missing component 1.9% and

the filled component was 0.01 %, Figure 16.

Figure 16: The decayed, missing, filled components of the dmft

3.5.2 Gender and ECC:

The mean dmft for 84 (49.1%) boys aged 3-5 years was 11.29 ±5.04 se=0.55, and 87

(50.9%) girls had a dmft of 10.78±4.18 se =0.45, Figure 17. The differences in the

dmft between gender were not statistically significant with an independent samples

test where a Levene‘s I indicated F= 4.207, df= (169, 161), p=0.479.

39

Figure 17: Distribution of dmft by gender and chronological age

3.5.3 Chronological age and ECC

The mean dmft for the 71 (41.5%) children aged 3-3.9 year was 10.55± 4.44, se=

0.53 while 87(50.9%) individuals aged 4-5-year olds had a mean dmft of 11.37±

4.73, se=0.47, Figure 17. The differences in the dmft between age groups were not

statistically significant with an independent samples test where a Levene‘s indicated

F=1.216, df= (169, 156), p=0.248

3.5.4 Dental maturity and ECC

Out of the 171 children aged 3-5 years, 136 (79.3%) of the children had a dental

maturity that was not delayed with a mean dmft of 11.06±4.74, se= 0.41; while

35(27.3%) whose mean dental maturity was delayed had a mean dmft of 10.91±4.17,

se=0.70. The differences in dmft between the children whose dental maturity was

statistically not significant when analysed with an independent samples test where a

Levene‘s for equal variances was assumed with F=1.534, df= (169, 59), p=0.860.

There were insignificant associations between delayed dental maturity and dmft, with

a Spearman‘s, correlation r=-0.003, p=0.965

40

3.5.5 Estimated dental age and ECC

There were 158(92.4%) children who had severe ECC, and the estimated dental age

was 4.58±0.76 years, se=0.06 years; while 13 (%) children had mild to moderate

ECC and their mean estimated dental age was 4.62±0.65 years, se=0.18 years. The

differences in the means were insignificant with an independent samples test where a

Levene‘s had F= 1.209, (169, 15), p=0.861 at 95 % CL.

3.5.5.1 Hypothesis

Spearman's correlation indicated that there were no relationships between dental age

and dmft with correlation coefficient r=0.045, p=0.563. Therefore, the null hypothesis

that there was no correlation between early childhood caries and dental age in 3-

5year-olds in Nairobi, Kenya with a statistically not significant Spearman‘s

correlation coefficient r=0.045, p=0.563, is accepted.

3.6 NUTRITIONAL STATUS

3.6.1 Weight for Height Z Scores

3.6.1.1 All Children Weight for Height Z Scores

The weight for height for age z scores (WHAZ) involved 171 children whose mean

WHAZ score was -0.22±0.97SD of whom none had severe wasting, while six (3.5%)

had moderate wasting and 23 (12.9%) had a z score of <+1SD. None was overweight

or obese, Figure18.

41

Figure 18: Distribution for the weight for height for age z scores for children

aged 36-59 months with WHO reference standards, n=171

3.6.1.2 Weight for Height Z Scores by age group

In the 36-47 months age group, there were 73 children with a mean WHAZ score of -

0.22±0.97SD. There were no children with severe wasting or moderate to severe

obesity. However, 12 (15.1%) of the children had a z score of <+1SD and 2 (1.4%)

had moderate wasting. The 48-60 age group had 98 (57.3%) individuals, and there

was none with severe wasting however 5 (5.1%) of the boys and girls had moderate

wasting with <-2SD while 11 (11.2%) had a z score of <+1SD. The rest had a healthy

weight for height with none being overweight or obese, Figure, 19.

42

Figure 19: The weight for height mean z-scores for the age group for children

aged 36=59 months

3.6.1.3. Weight for Height Z Scores by gender

There were 84 males in the study, and none had severe wasting or moderate to severe

obesity. The mean WHAZ for the males was -0.23±0.94SD with 3 (2.4%) children

having moderate wasting and 9 (10.7%) children having a z score of <+1SD. The

number of female children in the study was 87. They had a mead WHAZ of -

0.21±1.01. None of the females moderate or severe obesity and none had severe

wasting. However, 4 (4.6%) children had moderate wasting, and 13 (14.9%) had a z

score of <+1SD, Figure 20.

43

Figure 20: The weight for height for age mean z scores for females and males

age aged 36-59 months compared to WHO reference standards, females (n=84),

males (n=87)

3.6.2 Weight for Age Z Scores

3.6.2.1 All Children Weight for Age Z Scores

The mean weight for age Z scores (WAZ) score for 171 children aged between 36-59

months was <-0.43±0.94SD, however, one child (0.6%) was severely underweight

with a z score of <-3SD, while five (2.9%) of the children were moderately

underweight with <-2SD. When the weight for age is compared with the WHO

standards, it is skewed to the left of the median, Figure 21.

44

Figure 21: Distribution for the weight for age z scores for children aged 36-59

months with WHO reference standards, n=171

3.6.2.2 Weight for Age Z Scores by age group

When the children were categorised into age groups according to the WHO Anthro

software, the children in the 36-47 months age group was 73 (42.7%) with a mean

WAZ score of <+0.39±0.86SD. The children aged between 48-60 months were 98

(57.3%), and they had a mean WAZ score of <-0.45±1SD, Figure 22.

45

Figure 22: The weight for age z-scores for age groups for children aged 36-59

months

3.6.2.3 Weight for age Z Scores by gender

Out of the 171 children, 84 were males with a mean WAZ z-score of -0.46±0.97 SD.

Two (2.4%) children had moderate wasting (< -2SD). The mean WAZ z-score for the

87 female children was -0.4±0.92 SD. One female child (1.1%) had severe wasting

while three (3.4%) had moderate wasting. Figure 23.

46

Figure 23: Weight for age mean z scores for males (n=84) and females (n=87).

aged 36-59 months

3.6.3 Height for Age Z Scores

3.6.3.1 All Children Height for Age Z Scores

The mean z score for the 171 children aged 36-59 months was <-0.48±1.03SD out of

whom two (1.2%) of the children were severely stunted with a z score of <-3SD;

while ten (5.3%) of the children had moderate stunting with a Z score of <-2SD,

Figure 24.

47

Figure 24: Distribution of the children aged 36-59 months by height for age z

scores

3.6.3.2 Height for Age Z Scores by age group

The 36-47 months age had 73 (42.7%) children, and the mean WAZ score was

<+0.42±0.99SD, with five children 2.7%) having moderate stunting, and none of the

children had severe stunting in growth. Those aged 4-4.9 years were 98 (57.3%) with

a mean WAZ score of <-0.53±1.06SD of whom thirteen (7.1%) had moderate

stunting, and four (2%) were severely stunted, Figure 25.

48

Figure 25: The height for mean age z-scores for children aged 36-59 months

3.6.3.3 Height for Age Z Scores by gender

The mean HAZ z-scores for the male children were -0.53±1.08 SD. Out of the 84

boys, 5 (6%) had moderate wasting with a z score of % < -2SD. The girls, on the

other hand, had a mean HAZ z-score of -0.44±0.98 SD. Two girls (2.3%) had severe

wasting (< -3SD) while 4 girls (4.6%) had moderate wasting (< -2SD) , Figure 26.

49

Figure 26: The height for age mean z scores for males (n=84) and females

(n=87).

3.6.4 Nutritional Status and ECC

3.6.4.1. Weight for Height Z Scores and Severity of ECC

The mean length/height-for-age Z-score of thirteen (7.6%) children who had mild to

moderate ECC was0.03SD± 0.79SD, se=0.22 SD while 158(92.4%) who suffered

from severe ECC had a mean WHZ score of <-0.24SD±0.99SD, se=0.08 SD, Figure

27.

The difference between the mean length/height for age z score of 13(7.6%) who

suffered from mild to moderate ECC was not significant when compared to the

length/height-for-age Z-score WHZ for 158 (92.4%) individuals who had severe ECC

with an independent samples test where a Levene's test for equality of variances

where F=0.862, df= (169, 15), p=0.267

50

3.6.4.2 Weight for Age Z scores and Severity of ECC

The mean weight for age z score of thirteen (7.6%) children who had mild to

moderate ECC was <-0.37SD±0.73SD, se =0.20SD while 158(92.4%) who suffered

from severe ECC had a mean WAZ score of <-0.43SD± 0.96SD, se=0.08SD. Figure

27.

The mean WAZ of <-0.37SD±0.73SD for 13(7.6%) who suffered from mild to

moderate ECC was not significant when compared to the mean WAZ of <-0.43SD±

0.96SD for 158 (92.4%) individuals who had severe ECC with an independent

samples test where a Levene's test for equality of variances where F=1.335, df= (169,

15), p=0.800

3.6.4.3 Height for Age Z Scores and the Severity of ECC

The Length/height for age z score (HAZ) for thirteen (7.4%) children who had mild

to moderate ECC was <-0.66SD±1.07SD, se=0.30SD; while 158 (92.4%) who

suffered from severe ECC the mean HAZ was <-0.47SD±1.03SD, se=0.08, Figure

27.

The difference between the mean HAZ of<-0.66SD±1.07SD for 13(7.6%) who

suffered from mild to moderate ECC was not significant when compared to the HAZ

of <-0.47SD±1.03SD for 158 (92.4%) individuals who had severe ECC with an

independent samples test where a Levene's test for equality of variances where

F=0.064, df= (169, 13), p=0.540

51

Figure 27: Distribution of the children by nutritional status Z-scores for WHZ,

HAZ, WAZ, and ECC, n=171

3.6.4.4 Statistical test

The nutritional status did not have a statistically significant relationship with early

childhood caries for the 171 children with a Spearman‘s correlation: between weight

for height z score and dmft r=-0.064, p=0.405; between height for age z-score and

dmft r= -0.063, p=0.411; and between weight for age z score and dmft r=-0.099,

p=0.198.

3.6.4.5 Hypothesis

Therefore, the null hypothesis that there exists no association between early

childhood caries and nutritional status in 3-5-olds in Nairobi, Kenya is accepted as

the respective Spearman‘s correlation r for wasting, stunting, underweight and

leanness/ wasting was WHZ, r= -0. 064; HAZ r= -0.063, and WAZ, r=-0.099.

52

3.6.5 Nutritional Status and Dental Age

3.6.5.1 Weight for Height for Z scores and dental age

One hundred and thirty six(79.5 %) children whose dental age was not delayed and

the mean for WHZ as <-0.2SD±1SD, se=0.09SD;( range -2.72SD to 1.93SD) while

thirty five with delayed dental age had a mean WHZ , of <-0.31SD±0.88SD, se= 0.15

SD, (range <-2.14SD to 1.68 SD) Figure 28 .

3.6.5.1.1 Statistical test: independent samples test

The differences between the weight for height z score (WHZ) for children whose

dental age was not delayed and those whose dental age was delayed was not

statistically significant with a Levene's test for the equality of variances where

F=2.174, df= (169, 50), p=0.506 at 95% CL.

Figure 28: The nutritional status of the children with mean dental age, n=171

3.6.5.2 Weight for age Z score and dental age

One hundred and thirty six(79.5 %) children whose dental age was not delayed and

the mean for WAZ as <-0.35SD±0.95 SD, se=0.08 SD;( range <-3.40SD to 2.29 SD)

53

while thirty-five with delayed dental age had a mean WAZ , of <-0.72SD±0.86SD,

se= 0.15 SD, (range <-2.79SD to 0.97 SD) Figure 28.

3.6.5.2.1 Statistical test: independent samples test

Significant differences for length/height for age z score -WAZ between the children

whose dental age was delayed and those whose dental age was not delayed with an

independent sample test with a Levene‘s test for equal variance where F=0.532, df=

(169, 57), p= 0.041

3.6.5.3 Height for age Z score and dental age

There were 136(79.5 %) children shoes dental age was not delayed, and their mean

HAZ was -0.39SD±1SD, se= 0.09SD, ( range <-3.30SD to 2.21 SD); while 35

children whose dental age was delayed had a mean HAZ of <-0.86SD±1.07, se=0.18

SD, ( range <-3.02SD to 1.62 SD ) Figure 28 .

3.6.5.3.1 Statistical test: independent samples test

A Levene's test for equality of variances indicated significant differences for

length/height for age z score -WHAZ between the children whose dental age was

delayed and those whose dental age was not delayed where F=0.504, df= (169, 50),

p=0.016 at 95% CL.

3.6.5.4 Associations between Nutritional Status, Dental Maturity and Dental

Maturity

3.6.5.4.1 Nutritional status and dental maturity

The mean dental maturity for the 147 children was -0.5± 0.59 years, se=0.05. Dental

maturity of 113 (76.9%) children with normal nutritional status was -0.5±0.57,

se=0.05. Twenty-seven (18.4%) with mild wasting, had a mean dental maturity of -

0.49±0.63, se=0.12; and seven (4.8%) children with moderate wasting, had a mean

dental maturity of -0.5±0.75 years, se=0.28.

54

3.6.5.4.1.1 Spearman’s statistical test

Significant relationships between the height for age z score for the 171 children were

observed where a Spearman‘s correlation r=0.321, p=0.000 and weight for age z

score r=.224, p=0.003 at 95% CL. However, the association between WHZ score and

dental maturity was not significant with r=.032, p=.679.

3.6.5.4.2 Nutritional status and dental age

The children involved in the nutritional status were 147, and their mean dental age

was 4.58 ± 0.76years, se=0.06. The national status of the children was categorised as

normal mild, moderate. There were 113 (76.9%) had normal weight for height for age

z score and their mean dental age was 4.59 ± 0.76 years, se=0.07. Twenty-seven

(18.4%) with mild wasting, had a mean dental age of 4.49±0.79, se=0.15 and seven

(4.8%) children with moderate wasting, had a mean dental age of 4.74 ± 0.87 years,

se=0.33 years, Figure 29.

3.6.5.4.2.1 Spearman’s statistical test

A significantly strong and positive association was noted between height for age z

score and dental age for the 171 children where a Spearman‘s correlation r=0.314,

p=0.000 and weight for age z score r=0.202, p=0.008 at 95% CL. However, the

association between weight for height z-scores and dental age was not significant

with r=0.009, p=0.909 at

95% CL.

55

Figure 29: The estimated dental age and the nutritional status of children, n=171

3.6.5.5 Null Hypothesis

Therefore, the null hypothesis that there exists no association between the dental age

and nutritional status in children aged 3-5 -year-old in Nairobi, Kenya is accepted

when WHZ is used as the parameter for nutritional status as a Spearman‘s correlation

was not significant with WHZ, r=0.009, p=0.909 at 95% CL.

However, the null hypothesis that there exists no association between the dental age

and nutritional status for children aged 3-5-year-old in Nairobi, Kenya is rejected as

the height for age z scores and weight for age z scores as parameters correlated with

dental age where a Spearman‘s correlation HAZ r=0.314, p=0.000; WAZ r=0.202,

p=0.008 respectively.

56

CHAPTER FOUR

4.0 DISCUSSION

4.1 SOCIO- DEMOGRAPHICS

Several socioeconomic factors have been associated with the prevalence of dental

caries. These include the level of education of the parents, household overcrowding

and the number of children (63)

.

4.1.1 Distribution of the caregiver’s marital status and the person who provided

care

For one hundred and thirty-two (82%) children the caregiver of the child was the

mother while 28 (16.10%) children the caregivers was the father, two (1.20%) had an

aunty as the caregiver and one (0.60% ) child had the grandmother as the caregiver.

The current observations are similar to other studies that show the mother is the

primary caregiver (77.6-90.5%) in preschool children (83)

and children from low-

income families in Brazil (84)

.

One hundred and twenty-nine children in this study lived with both the father and

mother while forty-one lived with single parents. In Kenya, 66% of women and 51%

of men are either married or living in an intimate union with women (11%) more

likely to be divorced, separated or widowed than men (5%) (85)

. Families with

multiple caregivers especially those with elders have been shown to influence access

to preventive dental care (86)

. The mean number of people per household with whom

the children lived, in this study, was four. The mean number of persons per household

is comparable to the average household size in Kenya which is 3.9 members (85)

.

4.1.2 The caregiver’s level of education and the type of job

One (0.60%) caregiver had no formal education, twenty-eight (16.40%) had primary

school level of education, 75 (43.90%) had a secondary school education, 53

(31.00%) had a technical college level of education, and 14 (8.20%) had a university

57

level of education. The observation in this survey is comparable to a study done in

the same hospital where 48.9% of the fathers and 41.9% of the mothers had

completed secondary school education(87)

. According to the Kenya Demographic

Health Survey (2015), 7% of adults are illiterate, 25.7% have incomplete primary

school education, 24.6% completed primary schooling, 15.8% did not complete

secondary schooling, 15.7% completed secondary schooling while only 11.2% had

more than secondary school education (85)

. Men are more likely to be literate (92%)

compared to women (88%) (85)

.

The caregiver‘s job was described as professional in 27 (16. %), clerical or white-

collar jobs in 18(10.70%), businessman/woman in 74 (43.80%), skilled manual jobs

in 17 (10.10%), unskilled work in 6 (3.60%) and unemployed in 27 (16.0%) of the

respondents. In Kenya, 80% of men and 61% of women are unemployed (85)

. Of the

employed Kenyans, men are mostly employed in domestic service, unskilled manual

and agricultural positions while women are mostly in the domestic service and

agricultural positions (85)

. The differences translate to almost half the population

being in the two highest wealth quantiles (49% of men and 48% of women) with only

a small proportion being in the lowest wealth quantile (14% of men and 16% of

women) (85)

.

Several studies have shown that children from lower socioeconomic classes

experience a higher prevalence of dental caries (63,88–90)

. The documentation proves

that socioeconomic factors and social deprivation are indeed crucial determinants of

dental caries prevalence.

Significant associations have been reported to exist between the mother‘s level of

education and the prevalence of dental caries (63,89,90)

, or the level of education of both

parents (91)

. The lack of knowledge and the beliefs about primary teeth have been

shown to create barriers to early preventive dental care (86)

.

Costa et al. however showed that the mother‘s level of education was not

significantly associated with ECC (84)

. It is important to note that despite mothers

being aware of risk behaviours that result in poor infant oral health outcomes, not all

58

risk behaviours are changed. The low level of change in risk behaviour may be

influenced by a mother‘s level of autonomy in decision-making within the family, her

cultural influences and beliefs, her past dental experience and access to dental care as

well as her coping skills and supportive networks (92)

. In Kenya, women head one-

third of the households (85)

.

A negative correlation was found between a mother‘s nutritional status, level of

education and wealth and the proportion of underweight and wasted children (93)

.

In Kenya, children living in rural areas and those from poorer households are more

likely to be malnourished (93)

. A longitudinal cohort study in Portugal showed that

girls and boys from high socioeconomic status (SES) groups were fatter, heavier and

taller than those from low and average SES groups (94)

. Enwonwu in his study of

Nigerian children had similar findings where physical development (height and body

weight) was retarded in the malnourished and underprivileged village children

compared with their peers from higher socioeconomic areas (95)

.

Children from low and middle socioeconomic class (SEC) have a delayed mean

emergence age for the mandibular and maxillary incisors compared to children from

a high SEC. Socioeconomic effects are mixed for molars and premolars (96)

. Even

though a higher economic status has been associated with early dental development,

children in the USA, Belgium, Australia, and Iran had a lower mean age of

emergence of all permanent teeth compared to their Nigerian counterparts (96)

.

4.1.3 Natal History

One hundred and seventy-one children aged between 3-5 years of whom 3-4 year-

olds were 71 (41.1%), and 100 (59.1%) were aged 4-5 years. The gestation period for

both age groups was 36.7 weeks and the mean birth weight for those aged 3-4 years

was 3.3 kg while those in the age category of 4-5 years the mean birth weight was 3.2

kg. The immediate postnatal history of the children was that 137(80.1%) children had

had normal delivery while 34(19.9%) had been born through caesarian section. All

the one hundred and seventy-one children had been immunised. This percentage is

59

much higher than that of the Kenya Demographic Health Survey (KDHS) revealed

that 79% (KDHS 2014) (85)

and 77% (2008-09 KDHS) (93)

of children aged 12-23

months received all essential vaccines.

Sixty-two (36.9%) had suffered from different illness a few days before recruitment

into the study. The illnesses were diarrhoea which affected two (4.5%) children, and

cough/cold affected 42 (95.5%), and none of the children had recently suffered from

malaria. Thirty-one children had suffered from a chronic illness while 137 (81.5%)

did not suffer from chronic illnesses. However, none of the chronic illnesses were

conditions known to affect skeletal or dental development. During the Kenya

Demographic Health Survey of 2014, 99% of children under the age of 5 years had

shown symptoms of acute respiratory infection, 24% had a fever, and 15% had

diarrhoea, two weeks before the survey (85)

. The high prevalence of early childhood

diseases is evidence that children under the age of 5 years are vulnerable to common

communicable diseases.

For the children aged 3-4 years twenty-eight (39.4%) out of 71 had traumatic dental

injuries while in the age category of the 4-5year-olds, 33 (33%) out of 100

individuals had had traumatic injuries. In the 3-4 years, the age group the trauma

occurred at a mean age of 24.1 months while in the 4-5 months age group the trauma

occurred at 20.8 months post-uterine life. Out of the 171 children (16.4%) had

suffered from illnesses in the past. From the 3-4-year age group out of the 28, there

were 11(39.3%) who had suffered from illness for a period 11.5±14.5 months while

in the 4-5year-olds, 17(60.7%) had suffered from illnesses for 12.4±14.8 months.

4.1.4 Feeding Habits during the infancy period

WHO-UNICEF guidelines recommend that a child is exclusively breastfed for the

first six months of life, and after that continues with breastfeeding with nutritionally

adequate complementary foods up to a minimum of two years of age (97)

. One

hundred and forty-one (82.50%) children aged 3-5 years had exclusive breastfeeding

in their early infancy. Twenty-seven (15.80%) had breastfeeding and bottle- feeding

and three (1.8%) had exclusive bottle feeding. A study looking at breastfeeding

60

practices in Nairobi concluded that more than three-quarters of the mothers did not

use bottles (98)

. However, despite 99.0% of women in one study breastfeeding, only

12.6% breastfed their children exclusively for the first six months (99)

. There were 163

(95.90 %) children who breastfed on demand while seven (4.10 %) did not breastfeed

on demand. Nocturnal breastfeeding or breastfeeding more frequently has been

associated with increased dental caries risk (100,101)

.

The duration of bottle feeding for those aged 3-4 was eighteen months while those

aged 4-5 the duration of bottle feeding was twenty months. S-ECC has been

correlated with the use of a feeding bottle on demand during the day or its use at

night as a substitute for the pacifier (101)

. In early childhood, breastfeeding can protect

a child against dental caries. Studies have shown that breastfed children, when

compared to bottle-fed children, are less affected by dental caries (102)

.

The duration of breastfeeding in both the groups of 3-4 and 4-5-year-olds was

twenty-one and twenty-two months respectively. These findings are comparable to

that of a study of pre-school children in Nairobi where the mean breastfeeding

duration was 20.17 months (98)

. In Kenya, the median duration of breastfeeding is

21.0 months, being longest in the Eastern region (24.5 months) and shortest in

Nairobi (19.1 months) (85)

. Children breastfed for more than 12 months have an

increased risk of developing dental caries when compared to children breastfed for

less than 12 months (100,101)

.

Studies have shown statistically significant associations between age at which

breastfeeding is terminated and more extensive patterns of dental caries. Children

who have never been breastfed or those who are breastfed beyond the age of 24

months show a higher prevalence of dental caries (89)

. The frequency of breastfeeding

also affects ECC prevalence. A birth cohort study of children in Brazil showed that

the adjusted risk of S-ECC was higher for children breastfed 7 or more times a day

(88). Discontinuation of breastfeeding and the use of bottle feeding has been associated

with a more likelihood of wasting in children (odds ratio 1.6) (99)

. Studies have shown

61

statistically significant associations between age at which breastfeeding is terminated

and more extensive patterns of dental caries.

4.1.5 Distribution of the children by chronological age and gender

The mean chronological age for 171 children aged 3-5-year-old was 4.12±0.54 years

(range 3-4.98 years). However, when the children were categorised into age groups

the mean chronological age for 71 (41.5%) children 3-4-year-olds was 3.59±0.29,

se=0.03 years; the 100 (58.5%) individuals aged 4-5-year olds had a mean

chronological age of 4.50±0.29, se=0.03 years. The boys were 84(49.1%), and their

mean chronological age was 4.14±0.53, (range2.92-4.92 years) while 87 (50.9%)

girls had a mean chronological age of 4.14±0.55, (range 3-4.92 years).

The differences in the mean chronological age by gender was insignificant with an

independent samples test p=0.190 at 95% CL. The mean differences in chronological

age by age category were significant with an independent samples test p=0.000 at

95% CL.

4.2 ESTIMATED DENTAL AGE

4.2.1 The mean estimated dental age by gender

Eighty-four boys aged 3-5 had a mean estimated dental age of 4.68±0.72 years, se=

0.08, (range 2.20 to 5.90 years); while the girls had a mean dental age of 4.50±0.77,

se=0.08, (range 2.6- 6.70). Associations between gender and the estimated dental age

were negative and not significant, with a Pearson‘s correlation coefficient, p=0.102,

at 95% CL. Similarly, the differences in the estimated dental age by age group and

gender were not significant p=0.189 at 95% CL.

These findings are similar to those of an Indian study where no statistical differences

in dental age estimation were found between males and females (104)

. Kihara et al.

while using Willem‘s method of dental age estimation on a Kenyan population found

similar results with no statistically significant difference between the tooth maturity

for girls and boys in most of the dental maturity stages. The girls were noted to be

significantly ahead of the boys in the root development of the canines (105)

.

62

When the eruption of teeth is used to determine dental age, girls are ahead of boys in

both African and Asian races in Kenya (106)

. The mean age of emergence of all

permamnent teeth was earlier in Nigerian girls (96)

.

4.2.2 Estimated Dental Age Delayed and Not Delayed

The mean dental age for the 171 children was 4.59±0.75, se=0.06 (range 3.3-6.7

years) out of whom 136 (79.5%) whose dental age was not delayed had a mean dental

age of 4.79± 0.62, se=0.05, (range 3.30 -6.70 years). However, those whose dental

age was delayed had a mean dental age of 3.82 ± 0.73, se=0.12, (range 2.20 -4.80

years)

The differences in the mean age between those with a delayed dental age and those

without delay in dental age were statistically significant with independent samples

test with a Levene's test for equality of variances with F=4.649, df= (169, 47),

p=0.000.

The Demirjian method of dental age estimation has been shown to overestimate

dental age in several studies involving various world populations, including the

former Yugoslav Republic of Macedonia (1.17± 0.98 years) (107)

, Spanish Caucasian

(0.76±0.01 years for boys, 0.88±1.09 years for girls) (108)

, southern Chinese (29)

,

Belgian Caucasian (26)

, Norwegian (2)

, Turkish (50)

, and Nigerian (48)

children among

many other populations.

Other studies have however found the Demirjian method to underestimate age in

populations like Venezuelan Amerindian (mean underestimation of -0.1±1.04 years

for girls and -0.23±0.93 years for boys) (108)

. The Kuwaiti children in a study by

Qudeimat and Behbehani had a delayed dental age (0.69 years, SD=1.25 years,

CI=0.58 years) (109)

and advanced in Northern Turkish children (mean difference

between dental and chronologic ages; boys 0.36-1.43, girls 0.50-1.44) (110)

.

The Demirjian method was developed using dental maturity scores of the French-

Canadian population. In their original study, Demirjian et al. admitted that although

the maturity scoring system could be applied universally, the conversion of maturity

63

scores to dental age depended on the population under consideration (34)

. In as much

as scores for the different stages may not vary much between populations, the

maturity standards (centiles for maturity at given ages) may vary appreciably (36,111)

.

The upside of this is that this scoring system can be used with relatively small

population samples to study the differences in average maturity between different

populations (36)

. Since the Demirjian method is the most commonly used method in

dental age assessment study researches, comparisons between populations are made

easy.

However, Liversidge (112)

concluded that adapting dental maturity scores for age or

age for scores for different population groups is probably unnecessary since

Demirjian‘s dental maturity method is inappropriate to assess population differences

in dental maturity. Liversidge concluded that the Demirjian method of 1973 is

designed to assess maturity for a single child and is unsuitable for comparing groups

(82).

In 1976, Demirjian and Goldstein introduced an updated system of the original 1973

version. The new system was applicable from 2.5 to 17 years compared to the 1973

one which was applicable from ages 3-17 years. Also, the standardising population

sample was more significant and included two different sets of four teeth, which was

different from the original seven teeth of the 1973 version. Some studies have shown

that the seven teeth system overestimates age more than the four teeth system (107)

.

It has therefore been recommended that the polynomial compound formula is used to

predict dental age with more accuracy for results of international maturity standards

on different populations (113)

. There is a need for new graphs and tables produced for

specific populations which transfer the maturity scores calculated by the Demirjian

method into dental age are more accurate (114)

.

There have been attempts made to make the Demirjian method more accurate at

estimating chronological age by tailoring it to specific populations. Chaillet et al. (115)

came up with the International Demirjian method deriving values from 2-25 year-olds

from 8 countries and different ethnic backgrounds. Demirjian‘s method was used for

64

determining dental maturity scores, establishing gender-specific tables of maturity

scores and development graphs. This method had an accuracy of ± 2.5 years, making

it useful for forensic purposes. However, because of the inter-ethnic variability, it was

less accurate than Demirjian‘s method developed for a specific country.

In Kenya, permanent teeth in African children have been shown to erupt earlier by

0.2-0.7 years than in Asians (106)

. Kaur and Singh showed in their study that despite

the rapid physical growth observed in American and British girls, dental emergence

was ahead in Indian girls (116)

. The eruption of teeth is generally earlier in Africans

compared to other races. However, McGregor et al. in their study showed the number

of teeth erupted in Gambian children lagged behind American and European children

by up to 18 months of age, (117)

. Nyström et al. in Finland demonstrated that even

within the same homogenous population, there are differences in dental maturity with

children in rural areas having a higher dental age than those living in the city (p<

0.05) (118)

.

The Demirjian method is the most commonly used method of dental age estimation

because it is simple, fast and easy to apply (108)

. However other methods are more

accurate in specific populations such as the Chaillet method (Venezuelan Amerindian

and Spanish Caucasian) (108)

, Willem‘s method for former Yugoslav Republic of

Macedonia (107)

, Cameriere‘s method for Peruvian children (119)

.

Willem‘s method was shown to be slightly more accurate (98.62%) in estimating

dental age in an Egyptian population compared to Cameriere (98.02%) in 5-16-year-

olds (120)

. Liversidge et al. evaluated the bias and accuracy of age estimation using

developing teeth and demonstrated that the method that performed the best was the

dental maturity scale of Willem‘s et al. (26)

with a bias of 0.14±0.86 years. In a

another study(60)

, Willems method (26)

was again shown to be the most accurate, with

Demirjian‘s method (36)

overestimating age while both Nolla‘s (38)

and Haaviko‘s (121)

methods underestimated age. Rai et al. in India also found Willem‘s to the most

accurate radiographic method of age estimation, followed by Haaviko, Cameriere,

Nolla and lastly Demirjian (104)

.

65

Other methods of dental age estimation in children include the measuring of the open

apices to teeth which is Cameriere‘s method (105)

. The difference between observed

age and predicted age (OA-PA) with this method varies across populations, Italian (-

0.035 years) (122)

, European Caucasian (-0.114 years) (123)

,

among others. An

evaluation of the Chaillet‘s international maturity standards on Bosnian-

Herzegovinian population found it inaccurate as it also overestimated age with the

difference between dental and chronological age being 0.65±052 years for girls and

0.73±0.60 years for boys (113)

.

An analysis by age group by Liversidge et al. (124)

showed that most methods

estimated age with significant bias and standard deviation bias ranged from 0.86 to

1.03 years. An analysis by age group in the same study showed most methods under-

aged older children and over-aged younger children. Bias is the mean difference

between dental age and real age (124)

. Measures of the accuracy of a dental age

estimation method include the mean/median absolute difference, percentage aged to

within 10% and to within six months of the real age (124)

.

Skeletal age can also be used to estimate chronological age. Some studies have

shown that dental age is more accurate than skeletal age in estimation. Others have

discovered that the combined technique of using both hand-wrist bones and teeth has

higher accuracy than using either the teeth or bones alone (125)

.

Dental age and skeletal age are both under the influence of many factors including

genetics, malnutrition, socioeconomic factors. However, dental age is less affected by

environmental factors compared to skeletal age and is, therefore, more reliable in

estimating chronological age (126)

.

4.3 ORAL HYGIENE STATUS

4.3.1 Plaque score and gingival index

One hundred and sixty-one out of 171 children in the study were examined for plaque

scores and gingival bleeding. In twelve children, the index teeth were missing. The

mean plaque score was 2.1±0.7; while the mean gingival index was 1.1±0.6, se=0.1.

66

The mean plaque score for 71 (41.5%) children aged 3-4 years was 2.1±0.7, se=0.1,

and the gingival index was 1.01±0.6, se=0.1 while100(58.5%) children aged 4-5years

had a mean plaque score of 2.0±0.6, se=0.1 and a mean gingival index of 2.1±0.6.

A study by Kemoli and Chepkwony (127)

conducted in the same study area showed

high plaque scores in 55% of the subjects, and that plaque accumulation occurred on

more than one-third of the tooth surfaces but less than two-thirds. High plaque scores

were reported among children whose mother (42.0%) and fathers (48.2%) had

completed secondary school education, and whose fathers were in non-formal

employment (127)

.

4.3.2 Oral hygiene by age

A total of 159 individuals aged between 3-5 were assessed for oral hygiene, where 12

(7.5%) children had good oral hygiene, sixty-one (38.4%) had fair oral hygiene and

eighty-sixed (54.1%) had poor oral hygiene. The severity of gingivitis was that

85(52.8%) had mild gingivitis; 74 (46.0%) had moderate gingivitis, and two (1,2%)

had severe gingivitis. Children afflicted with ECC have been shown to have

difficulties in performing tasks of daily living like brushing their teeth due to the

associated dental pain and discomfort (128)

. This would explain the high percentage of

poor oral hygiene observed in this study.

4.3.3 The severity of gingivitis by age and gender

4.3.3.1 Chronological age

The 3-4-year- olds were 71 (41.5%)children aged 3-4 years 37 (52.1%) had mild

gingivitis, thirty-four (47.8%) moderate gingivitis however there were no children in

this age category that had severe gingivitis. The 4-5 age group had 90 (52.%)

children out of whom 48(53.4%) had mild gingivitis, there were 40 (44.4%) with

moderate gingivitis, and two (2.2%) had severe gingivitis. These findings are similar

to previous surveys that have shown that severe cases of gingivitis are less in younger

children than in older children and adults (129,130)

. As one progresses from early

childhood to adulthood, the severity of gingivitis increases (129,130)

.

67

4.3.3.2 Gender

There were 78 boys in whom the distribution of the severity of gingivitis was mild 36

(46.1); moderate 41 (52.6%) and severe gingivitis affected one (1.3%) boy. The girls

were 83 (48.5%), out of whom were 49 (59%)were affected with mild gingivitis, 33

(39.8%) had moderate gingivitis, and one (1.2%) had severe gingivitis. Gingivitis

cases are severe in this study in males more than in females. A study of Brazilian 5-

year- old children concluded that being of the male gender was one of the risk

indicators of gingivitis (131)

.

4.4 EARLY CHILDHOOD CARIES

4.4.1 Severity of ECC in the study group

The prevalence of ECC was 100% as the criteria for selection were children with

ECC. The mean dmft for the 171 individuals was 11.03 ± 4.62, se=0.35 (range 1-20

decayed teeth). The decayed component of the dmft was 10.81±4.43, se=0.34, (range

between 1 to 20 decayed teeth), missing component 0.22±0.78, se=0.34 (range 0-7

missing teeth), and the filled component of dmft was 0.01±0.08 se=0.01 (range 0-1

filled teeth). The decayed component formed 98% of the dmft, the missing

component 1.9% and the filled component was 0.01 %.

The low missing and filled components in this study can be explained by the fact that

most of these patients were visiting the dental clinic for the first time. More than 90%

of dental caries in the third world (low income) countries remain untreated (132)

. The

high prevalence of untreated caries is because the traditional method of restorative

dentistry required to treat dental caries is beyond the financial capability of low-

income nations, the majority of whom do not even have sufficient financial resources

for essential health care services for their children (132)

.

A study by Kemoli and Chepkwony carried out in the same dental hospital as this

study reported a prevalence of dental caries in the study group as 95.5%, and the dmft

was 8.53 (±5.52 SD). The male children in this study had a dmft of 8.65 (Sd+5.54)

while the females had a dmft of 8.37 (SD + 5.50). Wassuna et al. found a mean dmft

68

of 7.5(± 19) in 3-5-year-olds with S-ECC at New Nyanza Provincial General

Hospital(64)

. Njoroge et al. in their study in the neighbouring county of Kiambu found

a dental caries prevalence of 59.5% (133)

. The high prevalence seen in this study and

Kemoli and Chepkwony‘s study is because the study area is a dental hospital where

the majority of the patients present with dental caries.

4.4.2 Gender and ECC

The mean dmft for 84 (49.1%) boys aged 3-5 years was 11.29 ±5.04 se=0.55, and 87

(50.9%) girls had a dmft of 10.78±4.18 se =0,45.

The differences in the dmft between gender were not statistically significant with an

independent samples test, p=0.479. Several studies on early childhood caries have

shown that there is no gender predilection in the prevalence of dental caries (63)

.

A study by Kemoli and Chepkwony carried out in the same dental hospital reported

prevalence for dental caries in the current study was 95.5% with a mean dmft of 8.53

(±5.52 SD). The male children in this study had of dmft 8.65 (Sd+5.54) which was

slightly higher than the females who had a dmft of 8.37 (SD + 5.50)(127).

Willerhausen et al. in their study of elementary school children in a German city

showed a difference in the number of natural, healthy teeth between boys and girls

(p=0.0334) (66)

.

4.4.3 Chronological age and ECC

The mean dmft for the 71 (41.5%) children aged 3-4 year was 10.55± 4.44, se= 0.53

while 87(50.9%) individuals aged 4-5-year olds had a mean dmft of 11.37± 4.73,

se=0.47. Research documentation indicates that there is an association between the

age of the child and dental caries (63)

. Willerhausen et al. showed from their study that

the number of healthy teeth decreases with age (p=0.001) (66)

. With the increase in

age, the dmft also increases and this might be explained by the fact that the longer the

teeth have been in the oral cavity, the higher their chances of developing dental

caries.

69

4.4.4 Dental maturity and ECC

Out of the 171 children, 136 (79.3%) of the children whose dental maturity was not

delayed had a mean dmft of 11.06±4.74; while 35(27.3%) whose mean dental

maturity was delayed had a mean dmft of 10.91±4.17, se=0.70. The differences in

the dmft were not significant with the independent samples test with a Levene‘s

p=0.860; there were insignificant associations between delayed dental maturity and

dmft, with a Spearman‘s, correlation, p=0.965. These results show that dental caries

does not affect the mineralisation of the developing dentition.

4.4.5 Estimated dental age and ECC

There were 158 (92.4%) children who had severe ECC, and their estimated dental age

was 4.58±0.76 years, se=0.06 years; while 13 (7.6%) children had mild to moderate

ECC and their mean estimated dental age was 4.62±0.65 years, se=0.18 years. The

differences in the means were insignificant with an independent samples test where a

Levene‘s had F= 1.209, (169, 15), p=0.861 at 95 % CL. However, there is no

association between estimated dental age and ECC (Spearman‘s correlation p=0.563).

There is inadequate research on the relationship between dental maturity, dental age

and dental caries. Most studies look at the consequences of the premature loss of

primary teeth and its effect on the eruption of the permanent successors. Dental caries

if left untreated in children can result in the premature loss of primary teeth; with the

premature loss of deciduous molars, if extractions are done before completion of the

premolar crown formation, then the eruption of the premolar is delayed. However, if

the deciduous tooth is lost at a time when the premolar has advanced root formation,

then the eruption of the premolar is accelerated (69,70)

. However, according to Leroy et

al., even in cases where the predecessor tooth has dental caries or is restored but is

not extracted, the eruption of the successor is still accelerated by 2-8 months (72)

.

Tooth eruption is influenced by a variety of factors, both local and systemic, and is,

therefore, a less reliable indicator of dental age (26)

. In comparison, of all dental age

estimation methods, tooth mineralisation is influenced the least by environmental and

systemic factors and thus more reliable (6,26)

.

70

4.5 NUTRITIONAL STATUS

The Kenya National Demographic and Health Survey (KNDHS) of 2008-2009 report

stated that 35% of children aged five years and below were stunted, 16%were

underweight, and 7% are wasted (93)

. The 2014 demographic survey varies from the

Kenya Demographic Health survey of 2014 that had reports of 26% of children under

the age of 5 stunted, 11% underweight and 4% wasted (85).

Trends from the KNDHS

from 1993 to 2008 show little or no improvement in the nutritional status of Kenyan

children (93)

. An estimated 2.1 million children in Kenya are stunted (93)

. There has

been an increasing prevalence of obesity and overweight in Kenya as evidenced in

the KDHS (2008-2009). In Kenyan pre-school children, 4 % are obese, and 18% are

overweight (93)

.

In this study, using the WHAZ, 6 (3.5%) had moderate wasting; WAZ 1 (0.6%) was

severely underweight, 5 (2.9%) were moderately underweight; HAZ 2 (1.2%) were

severely stunted, 10 (5.3%) were moderately stunted. A study by Wassuna et al. of 3-

5-year-old Kenyan children with S-ECC had comparable results where 27 (14%)

were underweight, 10 (4.9%) were wasted, and 12 (6.1 %) were stunted (134)

.

4.5.1 Weight for Height Z Scores

The weight for height for age z scores (WHAZ) involved 171 children whose mean

WHAZ score was -0.22±0.97SD. None of the children had severe wasting which is

described by z scores of below minus three standard deviations (<-3D). Six (3.5%)

children had moderate wasting (<-2SD) and 23 (12.9%) had a z score of <+1SD.

None was overweight (>+2SD) or obese (>+3SD).

This index (WAZ) of nutritional status relates the body mass to body length or height.

It describes the current nutritional status (acute malnutrition) (85)

. An illness episode

or inadequate food intake may fail a child to receive adequate food nutrition, with

subsequent weight loss (wasting) in the period immediately preceding nutritional

assessment (85)

.

71

4.5.2 Weight for Age Z Scores

The mean WAZ score for 171 children aged between 36-59 months was <-

0.43±0.94SD, however, one child (0.6%) was severely underweight with a z score of

<-3SD, while five (2.9%) of the children were moderately underweight with <-2SD.

When the weight for age is compared with the WHO standards, it is skewed to the

left of the median.

The WAZ index takes both chronic and acute malnutrition into account and is

therefore considered a composite of the height-for-age and weight-for-height indices

(85).

4.5.3 Height for Age Z Scores

The mean z score for the 171 children aged 36-59 months was <-0.48±1.03SD out of

whom two (1.2%) of the children were severely stunted with a z score of <-3SD;

while ten (5.3%) of the children had moderate stunting with a Z score of <-2SD.

The height for age index is an indicator of cumulative deficits in growth and linear

growth retardation (85)

. It is therefore not sensitive to recent, short-term changes in

intake of the diet. Stunting is when an individual is short for their age, which

indicates chronic malnutrition and can be affected by recurrent and chronic illness

(85).

4.5.4 Nutritional status and ECC

Over time, interest in dental research has changed from the aetiology of dental

diseases to how dental diseases affect the general health of individuals. According to

Kathmandu et al. there are over 90% of dental caries untreated in preschool children

with toothache being very common (132)

.

A review article by Alvarez of several cross-sectional surveys revealed that

populations with higher dental caries prevalence in their deciduous dentition showed

a lower prevalence of dental caries in their permanent teeth (135).

However,

longitudinal data from individuals show a higher caries index in the permanent

dentition. In malnourished children, caries development is delayed as a consequence

72

of delayed tooth eruption, with a shift to the right in deciduous dentition dental caries

prevalence versus age curve. The same findings were confirmed in a study by

Alvarez (61)

where there was a shift to the right of the median for the age distribution

of dental caries by 2.5 years for malnourished groups (wasted and stunted, wasted,

stunted), compared with healthy children (p< 0.01). This shift was associated with a

delay in both the eruption and exfoliation of the deciduous teeth in children who were

malnourished (61)

.

4.5.4.1 Weight for Height Z scores and severity of ECC

The mean length/height-for-age Z-score of 13 (7.6%) children who had mild to

moderate ECC was 0.03SD± 0.79SD, se=0.22 SD while 158 (92.4%) who suffered

from severe ECC had a mean WHAZ score of <-0.24SD±0.99SD, se=0.08 SD.

The difference between the mean length/height for age z score of 13 (7.6%) who

suffered from mild to moderate ECC was not significant when compared to the

length/height-for-age Z-score WHAZ for 158 (92.4%) individuals who had severe

ECC with an independent samples test where a Levene's test for equality of variances

where F=0.862, df= (169, 15), p=0.267

A longitudinal study by Alvarez brought out an association between malnutrition and

dental caries where a prolonged episode of malnutrition in infancy (when most of the

primary teeth are still being formed) that leads to both stunting and wasting, results in

more caries by the age of 4 years (62)

. The explanation behind this could be possible

as a result of deleterious effects on amelogenesis (62)

which would increase a tooth‘s

susceptibility to dental caries 3-4 years later. In the study by Alvarez et al., strong

associations were noted between malnutrition and increased dental caries in the

deciduous teeth. Children who suffered a single but prolonged malnutrition episode

during infancy (i.e. stunting and wasting) had higher caries experience by the age of 4

years than did the children who had a less severe form of malnutrition or had no

malnutrition at all (62)

. However, where there were no differences in caries between

the normal, stunted and wasted groups was explained by the fact that malnourished

children experienced delayed eruption of the deciduous dentition, hence delayed

73

dental caries development (62)

. Similar findings were present in an earlier study by the

same author where it was concluded that malnutrition resulted in increased dental

caries experience, affected age distribution of dental caries, and delayed tooth

development in the deciduous teeth (61)

. In this study, peak caries activity was

significantly higher in wasted and in wasted and stunted children, when compared

with healthy controls (61)

.

Hilgers et al. showed that even after adjusting for age and gender, the mean caries

average for permanent molars significantly increased with increased BMI (136). A

study of 30-70-month-old Iranian children showed a statistically significant inverse

association between BMI-for-age scores and the frequency of caries-free (P=0.001)

and a significant direct association with S-ECC children (P=0.001). Overweight

children in this study had a higher decayed, extracted and filled surfaces of deciduous

teeth score (deft) compared to those of normal BMI- for-age scores (137)

. A study

showed that obese adolescents were more likely to have caries than the non-obese

since there was a significant association between BMI and DMFT indices (P=0.01) in

the severely obese group (138)

.

4.5.4.2 Weight for Age Z scores and severity of ECC

The mean weight for age z score of thirteen (7.6%) children who had mild to

moderate ECC was <-0.37SD±0.73SD, se =0.20SD while 158(92.4%) who suffered

from severe ECC had a mean WAZ score of <-0.43SD± 0.96SD, se=0.08SD.

The mean WAZ of <-0.37SD±0.73SD for 13(7.6%) who suffered from mild to

moderate ECC was not significant when compared to the mean WAZ of <-0.43SD±

0.96SD for 158 (92.4%) individuals who had severe ECC with an independent

samples test where a Levene's test for equality of variances where F=1.335, df= (169,

15), p=0.800.

The results from this study, therefore, do not support the belief that ECC adversely

affects the weight of the child. There are however studies that have shown that

underweight children are more likely to have S-ECC than children of normal

weight/height (63)

. Other studies have taken a slightly different approach whereby

74

children with S-ECC were shown to have significantly less height and weight than

their comparison peers (139,140)

. According to Wassuna et al., children with S-ECC

were 1.23 times more likely to be underweight compared to children without dental

caries (134)

. A similar study of 6-7-year-old first-grade children showed that for each

new carious tooth surface, the child had increased odds of being at risk for being

underweight by 3.1 % after adjusting for age and dental visits (141)

. Acs et al. in 1992

concluded that three-year-olds with nursing dental caries who had at least one tooth

with pulpal involvement weighed about one kilogram less than the control children

who did not have nursing caries (142)

. In this study, 8.7% of the children with caries

weighed less than 80% of their ideal weight compared to only 1.7% of the

comparison group, indicating that growth may be affected adversely by the

progression of nursing caries (142)

.

‗Catch-up growth‘ seen after comprehensive dental treatment has shown that the

previous oral diseases negatively impacted nutritional intake and this gives further

evidence on the effect of dental caries on growth (143)

. Following full rehabilitation,

children with S-ECC gain weight (140)

. Acs et al. demonstrated significantly increased

growth velocities following the therapeutic intervention in children with ECC

through the course of the follow-up period (144)

. The catch-up growth was such that

these children no longer statistically significantly differed in percentile weights from

the comparison group (144)

.

However, another study failed to find any significant weight gain (catch-up growth)

following complete dental rehabilitation in 2-7-year-old children with rampant caries

(145). Also, a study found that the mean percentile weight of 2-7-year-old children

with rampant caries was not below the 50th

percentile (146)

.

In addition to improvements in body weight, dental treatment improves the child‘s

Oral Health-Related Quality of Life (OHRQoL) since the child now experiences

less/no pain, and can adequately feed and sleep. White et al. in their study evaluated

the parents of children with dental caries who had undergone dental treatment and

they reported that the parents perceived that their children were smiling more,

75

performed better in school and had increased social interactions (147)

. These findings

of improved psychological and social wellbeing of the child have been supported by

several other studies (145,146,148)

.

A study on malnutrition involving 2-6 year-olds with S-ECC by Clarke et al., where

all the nutrition tests carried out, indicated that the children were malnourished (149)

.

However, compared to blood tests (serum albumin, serum ferritin, haemoglobin and

mean corpuscular volume), the anthropometric measurements (ideal body weight,

body mass index and measurement of the mid-arm circumference) detected fewer

cases of malnourishment (149)

. According to Clarke et al., BMI tests that used the fifth

percentile on childhood charts to define malnutrition were insensitive since many

malnutrition cases were missed. They, therefore, suggested that subjects with BMI

values less than the 15th

percentile instead of the 5th

percentile should be regarded as

malnourished; Clarke et al. found out that in young children, due to the presence of

low values of haemoglobin and ferritin, S-ECC may be a risk marker for the

development of otherwise unexplained iron deficiency anaemia. They, therefore,

recommended that iron levels should be assessed in patients with S-ECC regardless

of their anthropometric appearance (149)

.

Theories related to untreated dental caries have been put across to explain their

impact on the nutritional status of children. Sheiham in a review article gave three

reasons (143)

. First, there is an infection, pain and discomfort associated with untreated

dental caries which interferes with the intake of food (145,150)

. Children with SECC

have problems eating certain kinds of foods (129,150)

. Secondly, severe dental caries is

associated with disturbed sleep, irritability and pain (151,152)

. The disturbed sleep may

in return affect glucocorticoid production and therefore growth (143)

. Third and lastly,

that severe dental caries affects growth because chronic inflammation with pulpitis

and dental abscesses which in turn affects erythropoiesis. The rationale is that the

inflammatory cytokines like interleukin-1 (IL-1) can induce inhibition of

erythropoiesis with resultant low haemoglobin and anaemia of chronic disease (143)

.

Anaemia of chronic disease (Anaemia of inflammation) is a mild-to-moderate

76

anaemia which often develops in the setting of acute or chronic immune activation

(chronic disease, infections or malignancy) and is the second most common type of

anaemia (after anaemia of iron deficiency) (153)

. It is mediated by inflammatory

cytokines and is characterised by low serum iron (hypoferremia) and often increased

reticuloendothelial stores of iron (153)

.

In Kenya, micronutrient deficiencies are highly prevalent among women and children

aged five years and below. The most common deficiencies in Kenya according to the

1999 National Micronutrient Survey include vitamin A deficiency, zinc deficiencies,

iodine deficiency disorders and iron deficiency anaemia, (93).

Micronutrient

deficiency contributes to childhood morbidity and mortality (85)

. These micronutrients

from direct supplementation, natural foods or fortified foods (85)

.

The OHRQoL (Oral health-related quality of life) is significantly poorer in children

with untreated ECC than in children with ECC (148,150,151)

. In Brazil, Moura-Leite et

al. found that in preschool children, the impact of dental pain had a statistically

significant association with gender, social class, dental caries, missing teeth, filled

teeth and caries involving the pulp (128)

. These children with dental caries also have a

higher risk of emergency dental visits and hospitalisations due to dental pain and

infections (152,154)

. They also tend to have increased days with absence from school

and restricted activity (128,150,152,155)

. The child‘s educational development is interfered

with as there is a diminished ability to learn. Emotional stress, including anger and

instability due to interruption of school work, and play, has been associated with

dental pain (156)

. The child‘s self-esteem may also be negatively affected by the child

being teased by other children as a result of aesthetic and phonetics problems. As a

result, the child may adopt a silent demeanour or avoid smiling (156)

.

Oral pain due to dental causes has been shown to have a considerable impact not just

on the child, but also the parents (152,155)

. The parents are affected economically

because of time taken off from work to visit the dentist (152,155)

.

Childhood obesity has also been linked to childhood dental caries. This may be due to

common confounding factors such as poor oral hygiene and frequency of intake of

77

cariogenic foods and drinks and in these children (136)

. Willerhausen et al. also

showed in their study that even after adjusting for age, a significant association

between high weight and caries frequency in the deciduous (p=0.0067) and the

permanent (p= 0.0002) remained (66)

.

However, the relationship between the presence of dental caries and being

overweight is associated with a high intake of carbohydrates. It is however far more

complex and cannot be explained by carbohydrate consumption alone (63)

. This

association may not be a cause-effect relationship since both dental caries and obesity

are multifactorial conditions, and they both share risks with other chronic diseases

(63). According to Marshall et al., dental caries and obesity co-exist in children of low

socioeconomic status (91)

. However, children at risk of being overweight had higher

caries rates than normal or overweight children (91)

. A National Health and Nutrition

Examination Survey in the United States III; the data showed no difference in caries

rates by weight in younger children (155)

. However, being overweight may be

associated with decreased rates of dental caries in older children (156)

. A study by

Gerdin et al. had findings similar to those by Marshall et al. where obese, but not

overweight children had more caries affected teeth than the non-obese (158)

. In this

study, BMI had an independent, though weak, effect on caries variation in multiple

regression. Significant insight into overweight and obesity in children aged four-year-

olds whose weight at ages five, seven, and ten years of age was within health range

showed that they had significantly low caries prevalence than children who had

normal body weight from 4 to 10 years of age (158)

.

Several other studies have however reported that there was no significant association

between BMI-for-age and dental caries (84,157,159)

. They also found that children who

were overweight had a lower geometric DMFT (permanent dentition). The

contradicting results are an indication that there was no clear evidence of an

association between dental caries childhood obesity (160)

.

78

4.5.4.3 Height forAge Z scores and severity of ECC

The height for age z score (HAZ) for thirteen (7.4%) children who had mild to

moderate ECC was <-0.66SD±1.07SD, se=0.30SD; while 158 (92.4%) who suffered

from severe ECC the mean HAZ was <-0.47SD±1.03SD, se=0.08

The difference between the mean HAZ of<-0.66SD±1.07SD for 13(7.6%) who

suffered from mild to moderate ECC was not significant when compared to the HAZ

of <-0.47SD±1.03SD for 158 (92.4%) individuals who had severe ECC with an

independent samples test where a Levene's test for equality of variances where

F=0.064, df= (169, 13), p=0.540

In populations with high levels of nutritional deficiency, children with stunting have

been shown to have an increased dental caries experience in their deciduous dentition

(61,62).

4.5.4.4 Statistical test

The nutritional status did not have a statistically significant relationship with early

childhood caries for the 171 children with a Spearman‘s correlation: between weight

for height z score and dmft r=-0.064, p=0.405; between height for age z-score and

dmft r= -0.063, p=0.411; and between weight for age z score and dmft r=-0.099,

p=0.198 at 95% CL. These findings do not mirror those of other studies that have

shown that children with low Z scores in HAZ, WHZ and WAZ indexes had an

increased risk of having dental caries.

4.5.4.5 Null Hypothesis

The association between nutritional status and dental age is accepted as the null

hypothesis that there is no association between the nutritional status and the dental

age is rejected.

4.5.5 Nutritional Status and Dental Age

Most studies that asses the association between dental age and nutrition use eruption

instead of tooth mineralisation. Delay in the eruption and exfoliation of the deciduous

79

teeth has been observed in malnourished children (61).

A longitudinal study by Alvarez

et al. showed a significant delay in the eruption of the deciduous dentition in children

who had one episode of malnutrition occurring in early childhood, even though some

of the teeth erupted two years after the malnutrition episode (161)

. Enwonwu‘s study

involving Nigerian children showed that the eruption of primary teeth demonstrated a

correlation with height and weight changes (95)

.

Unlike eruption which is more susceptible to environmental factors, tooth

development is insulated and is, therefore, more reliable maturity indicator. A study

involving Northern Sudanese children comparing the timing of tooth formation in

normal and malnourished groups concluded that sustained malnutrition had a little

measurable effect on tooth mineralisation (162)

. A study by Cameriere et al. involving

Peruvian school children that used the Cameriere and Demirjian methods to estimate

dental age in normal and undernourished children found no significant influence of

under-nourishment on tooth mineralisation. They, however, suggested that the use of

a quantitative method to assess this influence would be more accurate than the

radiographic method they employed in this study (119)

.

However, a study by Hilgers et al. in the USA found that children who are

overweight or obese had significantly accelerated dental development compared to

children with normal BMI even after adjusting for age and gender (p< 0.01) (163)

. The

mean dental age acceleration for obese and overweight subjects was 1.53±1.28 years

and 1.51±1.22 years respectively when the Demirjian‘s method was used to assess

dental age and (163)

.

4.5.5.1 Weight for height for age z score and dental age

One hundred and thirty six(79.5 %) children whose dental age was not delayed and

the mean for WHAZ as <-0.2SD±1SD, se=0.09SD;( range -2.72SD to 1.93SD)

while thirty five with delayed dental age had a mean WHAZ , of <-0.31SD±0.88SD,

se= 0.15 SD, (range <-2.14SD to 1.68 SD).

80

4.5.5.1.1 Statistical test: independent samples test

A Levene's test for equality of variances indicated that there were no statistically

significant differences between the length/height for age z score -WHAZ for children

whose dental age was delayed and those whose dental age was not delayed where

F=2.174, df= (169, 50), p=0.506 at 95% CL.

Overweight children have been shown to have more erupted teeth (higher eruption

rates) than those with normal BMI (164)

. Costacurta et al. (76)

assessed childhood

obesity about skeletal (cervical vertebra maturation) and dental maturity. The

children were categorised as underweight, normal weight, pre-obese and obese

according to the body fat percentage (FM %) McCarthy cut-offs classification and

BMI. FM% was determined using Dual-energy X-ray Absorptiometry (DXA). The

BMI classification did not show any statistical difference among the groups as for

chronological, dental and skeletal age. However, according to FM% McCarthy

classification, it was observed that with an increase in the FM% (i.e. from normal

weight to obese children) the skeletal-dental age increased concerning the

chronological age. In the study, the difference between chronological and dental-

skeletal age was not statistically significant for normal underweight (p=0.46) and

normal weight (p=0.33) children. However, the differences were statistically

significant for the pre-obese (p=0.01) and the obese (p<0.001) children. The

explanation behind these results as discussed by Costacurta et al. may be because

BMI misclassifies adiposity status in the paediatric population compared to DXA.

Mack et al. (77)

found similar results in a study of orthodontic patients.

Hedayati and Khalafinejad (165)

in a similar study (BMI, dental development by

Demirjian and skeletal maturation of cervical vertebrae), the findings were slightly

different. It was observed in their study that there was a correlation between dental

maturity and increasing BMI percentile (p=0.002), where children who were

overweight and obese had accelerated dental development (165)

. However, this study

differed from that by Costacurta et al. in that there was no significant relationship

between BMI percentile and skeletal maturation.

81

4.5.5.2 Weight for age Z scores and dental age

One hundred and thirty six(79.5 %) children whose dental age was not delayed and

the mean for WAZ as <-0.35SD±0.95 SD, se=0.08 SD;( range <-3.40SD to 2.29 SD)

while thirty-five with delayed dental age had a mean WAZ, of <-0.72SD±0.86SD,

se= 0.15 SD, (range <-2.79SD to 0.97 SD).

4.5.5.2.1 Statistical test

Significant differences for height for age z score -WAZ between the children whose

dental age was delayed and those whose dental age was not delayed with an

independent sample test with a Levene‘s test for equal variance where F=0.532, df=

(169, 57), p= 0.041

Significant and a strong positive association was observed between the weight for age

z score and dental age for the 171 children where a Spearman‘s correlation r=0.202,

p=0.008 at 95% CL.

As earlier mentioned, low weight for age (wasting) has been associated with the

delayed eruption of the deciduous dentition (161)

. Delgado et al. in a study involving

Guatemalan children found that heavier children had a more significant number of

deciduous teeth erupted and that the timing of eruption of deciduous teeth was more

closely associated with postnatal weight than with birth weight (166)

. As an estimate of

mean chronological age in populations living under conditions of mild to moderate

malnutrition, the use of the low number of deciduous teeth erupted is relatively

accurate, with variations of between 1-2 months (166)

.

4.5.5.3 Height for age z score and dental age

There were 136 (79.5 %) children shoes dental age was not delayed, and their mean

HAZ was -0.39SD±1SD, se= 0.09SD, (range <-3.30SD to 2.21 SD); while 35

children whose dental age was delayed had a mean HAZ of <-0.86SD±1.07, se=0.18

SD, (range <-3.02SD to 1.62 SD).

82

4.5.5.3.1 Statistical test: independent samples test

A Levene's test for equality of variances indicated significant differences for

length/height for age z score -WHAZ between the children whose dental age was

delayed and those whose dental age was not delayed where F=0.504, df= (169, 50),

p=0.016 at 95% CL.

These findings mirror those in a longitudinal study by Alvarez that showed delayed

eruption of deciduous teeth with malnutrition (62)

. According to this study, one

malnutrition episode occurring during the first year of life is sufficient to cause a

significant delay in the eruption of all deciduous teeth. This pattern was observed

even though some of the teeth erupted two years after the malnutrition episode (62)

. It

was also shown in the same survey that compared to wasting; stunting was more

strongly associated with the delayed eruption of deciduous teeth. The observation is

to be expected since stunting is chronic malnutrition and has a more significant

impact while wasting is acute malnutrition (161)

. A study by Psoter et al. showed

similar results in Haitian adolescents were both delayed shedding of the deciduous

dentition and delayed the eruption of permanent teeth was associated with early

childhood protein-energy malnutrition (EC-PEM) and current stunting in

adolescence. The result demonstrated that malnutrition beginning in the earliest years

and extending throughout childhood influenced the eruption and exfoliation of teeth

(75).

Haddad et al. also showed that taller children have more erupted teeth than shorter

children, regardless of birth weight and birth length (167)

. Kaur and Singh had similar

findings where there was a strong, positive association between height and the

number of erupted deciduous and permanent teeth (116)

. Flores-Mir et al., however,

showed in their study of Peruvian school children aged 9.5-16.5 years that skeletal

maturity stages and dental development were not associated with stunting (168)

. In

their study, an adaptation of the Hägg and Taranger method was used to assess

skeletal maturity of the middle phalanx of the third finger while the Demirjian

method was used to assess dental maturity of the lower left canine.

83

4.5.5.4 Null Hypothesis

The association between dental age and WHZ was insignificant with a Spearman‘s

correlation with r=-0.001, p=0.994. However, significant associations were noted

between dental age and HAZ, r=0.314, p=0.000 and between dental age and WAZ

r=0.202, p=0.008 at 95% CL.

Therefore, the null hypothesis that there exists no association between the dental age

and nutritional status in children aged 3-5-year-old in Nairobi, Kenya is accepted

when WHZ, may be used as a parameter for nutritional status as a

However, the null hypothesis is rejected when HAZ and WAZ are used as

parameters for nutritional status since there was a strong positive and statistically

significant relationship; Spearman‘s correlation p=0.00 and p=0.002 for HAZ and

WAZ respectively

84

4.6 CONCLUSION

Most of the children had a dental age which was advanced when compared to the

chronological age. The advanced dental age in respect to chronological age may be

due to racial differences, and there may be a need to establish a reference dental age

dataset for Kenyan children of African descent. The dental age had associations with

only WAZ as underweight and HAZ as stunting in growth as nutritional status

parameters. Hence children who are underweight or stunted in growth may have

delayed dental maturity. The was also no relationship between dental age and ECC.

Hence the severity of early childhood caries may not be a good indicator of delayed

dental age. There was also no relationship between nutritional status and ECC.

85

4.7 RECOMMENDATIONS

The children of African descent have a dental age which is advanced beyond the

chronological age. Further research has to be carried out to confirm this finding. That

children in the age category of 3-4.9 years with severe malnutrition in terms of

stunting (HAZ) and severe underweight (WAZ) are vulnerable to delayed dental age

hence delayed eruption of the dentition. Hence when determining the dental age,

stunting and severe weight loss should be taken into consideration.

86

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104

APPENDICES

APPENDIX I: Ethical Approval Letter

105

106

APPENDIX II: NACOSTI Research Authorization Letter

107

APPENDIX III: Nairobi City County Research Authorization Letter

108

APPENDIX IV: Kenya Bureau of Standards Calibration Certificate

(BS/MET/2/3/798)

109

110

APPENDIX V: Kenya Bureau of Standards Calibration Certificate

(BS/MET/2/3/91/799)

111

112

APPENDIX VI: Schedule of Activities

Event Person(s) Responsible Time Limit

1. Proposal Writing Principle Investigator

Supervisors

December 2016 -October

2018

2. Submission to Ethical

Committee and BPS UoN

for approval

Principal Investigator

Research, Ethics and

Standards Committee

Kenyatta National

Hospital and the

University of Nairobi.

November, 2018

3.Clearance with Local

Authorities

Principal Investigator

Medical Superintendent-

Lady Northey

Dean, UNDH

February – March, 2018

4. Data Collection Principal Investigator

Supervisors

Field Assistants

April – August, 2018

5. Data Entry Principal Investigator September, 2018

6.Data Analysis and

Report Writing

Principal Investigator

October – December,

2018

7. Thesis Writing Principal Investigator

Supervisors

January – April, 2019

8.Thesis Submission Principal Investigator May, 2019

9. Thesis defence Principal Investigator Novemeber, 2019

113

APPENDIX VII: Budget

Category Costs (Kshs) No. of Units Total Cost

(Kshs)

Proposal Development

Internet access

Flash Disk

Printing

Binding

60/hr

3000

900

200

900 hrs

1

15

15

54,000

3,000

13,500

3,000

73,500

Personnel

1st Assistant

2nd

Assistant

3rd

Assistant

12,000

12000

12000

1

1

1

12,000

12,000

12,000

36,000

Training of Personnel 10,000

114

Principal Investigator

Research assistants

10,000

10,000

1

3

20,000

30,000

50,000

Data Collection

Printing of forms

Pre-testing questionnaires

Weighing scale

Plane mirrors

Examination gloves

Face masks

Disinfectant solution

Gauze

Sterilisation pouches

150

150

3,000

400/pc

750/pkt

400/pkt

2800/pkt

1500

2000/pkt

220

20

2

30

5

3

1

1

1

33,000

3,000

6,000

12,000

3,750

1,200

2,800

1,500

2,000

65,250

115

Internet

Biostatistician

Thesis typing and

printing

Thesis binding

Calibration (KEBS)

2000

450

8,700

10

10

2

30,000

40,000

15,000

4500

17,400

106,900

Total 341,650

116

APPENDIX VIII: Consent Information Document (English)

Dear Parent/Guardian of ..........................................................................................,

I am Dr Omuok Joyce Atieno, a Masters of Paediatric Dentistry student at the

University of Nairobi, School of Dental Sciences, Kenya.

Study background: Early Childhood Caries (ECC) if left untreated, may progress to

a more severe form, which may negatively impact on dietary intake (due to pain and

discomfort) with the risk of the child being underweight due to poor feeding. A

reduction in a child‘s quality sleep that is necessary for proper emotional and physical

development may occur. Inadequate sleep may affect growth hormone production,

and hence growth is affected, particularly body weight and height and possibly dental

development.

Broad objective: In partial fulfilment of my degree, I am working on a dissertation

entitled: ‗Chronological age, dental age and nutritional status among 3-5-year-olds

with early childhood caries in Nairobi, Kenya.‘ the study aimed at determining the

effect of early childhood caries on the nutritional status and dental age of 3-5-year-

old children.

Procedure: The study involved a dental examination done by the Principal

Investigator (Dr Omuok Joyce Atieno) on children with Early Childhood Caries. It

lasted 10-15 minutes for each child. The investigation involved an assessment of the

oral hygiene status, dental caries experience, nutritional status, chronological age and

dental age. I asked questions relevant to the research and subsequently recorded them

in a questionnaire. Then I proceeded to measure the height and weight of your child.

Height shall be measured using a standard height board and the weight using a Salter

weighing scale. Next, I will examine your child's mouth using sterile instruments and

materials and record the findings. Finally, I will examine the child's radiograph

(OPG) to determine the dental age.

117

No treatment will be given to the child during the study, but all participants will be

treated appropriately for dental caries at the Lady Northey Dental Hospital.

Voluntariness: Participation in this study is voluntary for both you and your child.

Assent process: Your child will not be forced to be examined if they resist or are

unable to open their mouth.

Confidentiality: Even though the information obtained will be made available to the

Lady Northey Dental Hospital and the University of Nairobi, the child's details and

the results of the study will remain confidential and used only for research purposes.

Your child's identity will be concealed, and his/her name will not appear anywhere on

the coded forms with the information. All documents, paper and computer records

will be kept under lock and key and with password protection respectively.

Benefits: The examination is solely for academic purposes and doesn‘t imply that

any dental treatment will be offered. The child shall be treated at the Lady Northey

Dental Hospital or referred where needed.

Risks: There will be no risks involved while undertaking this procedure and no fee

whatsoever was levied on those who participate.

The right of withdrawal: You may withdraw your child from participating at any

time without suffering any consequences.

This letter is to kindly request you accept and allow your child to participate in the

study. Read it and make sure you have understood it before signing and return the

signed document to the hospital if you agree to your child‘s participation in this

study.

For further information or inquiries; -

Dr Omuok Joyce Atieno, joyceomuok@gmail.com

Lead supervisors:

Prof. Gladys Opinya, amaniwanjala@yahoo.com

118

Dr Edith Ngatia, edith_muthoni@yahoo.co.uk

The Chairperson, Kenyatta Hospital/University of Nairobi Ethics and Research

Committee, uonknh_erc@uonbi.ac.ke

119

APPENDIX IX: Consent Information Document (Swahili)

IDHINI YA UTAFITI KUTOKA KWA MZAZI/MLEZI WA MTOTO WA

MIAKA MIATAU HADI MITANO ANAYEKABILIWA NA SHIDA YA

MATUNDU KWENYE MENO.

Kwa mzazi/mlezi wa ……………………………………………………………

Mimi ni Daktari Omuok Joyce Atieno, mwanafunzi wa stashahada, kwenye kitengo

cha meno ya watoto, Chuo Kikuu cha Nairobi, Kenya.

Kiini cha utafiti: Matatizo ya matundu kwenye meno ya watoto yasiposhughulikiwa

mapema humtatiza motto kwa uchungu na hata ukosefu wa usingizi wa kutosha.

Matatizo haya humfanya motto kukua pole pole ikilinganishwa na vile watoto wa

umri wake wanavyokua.

Lengo la utafiti: Hii ni sehemu muhimu katika kukamilisha mradi wangu wa tatizo

la matundu menoni miongoni mwa wa wtoto wa umri wa miaka mitatu hadi mitano,

kwenye jiji la Nairobi, nchini Kenya. Utafiti huu pia utaweza kuonyesha madhara ya

tatizo hili kwenye malazi na ulaji ya watoto hawa.

Utaratibu wa utafiti: Meno ya motto wako yatakaguliwa na Dkt Omuok Joyce ili

yaonekane kama yana tundu lolote. Pia, usafi wa mdomo wake utaangaliwa.

Watakaopatikana na tatizo la matundu kwenye meno yao watatibiwa kwenye

hospitali ya meno ya Lady Northy kwa malipo ya chini.

Ushiriki: Hakuna atakayekulazimisha kushiriki kwenye utafiti huu. Pia, hakuna

atakayemlazimisha motto wako kufungua mdomo ikiwa hataki.

Usiri: Ingawa matokeo ya utafiti huu yatatumika chuoni Nairobi na hospitali ya

meno ya Lady Northey, jina la motto na matokeo ya utafiti yatasalia kuwa siri.

Manufaa: Utafiti huu utamsaidia mzazi kujua iwapo motto wake ana tatizo la tundu

na kama meno ya mwanawe yanamea kwa njia inayofaa.

120

Hatari: Utaratibu wa utafiti huuhauna madhara yoyote kwa motto anayeshiriki.

Mtoto au mzazi ana uhuru wa kujiondoa kutoka kwenye utafiti huubilakupata adhabu

yoyote.

Ikiwa umeelewa (mzazi/mlezi) utaratibu wa utafiti huu na ungetaka motto wako

ashiriki, tafadhali tia sahihi kwenye barua hii.

Sahihi: ……………………………………….

Kwa maelezo zaidi/maswali, tafadhali wasiliana na;

Dkt. Omuok Joyce Atieno, joyceomuok@gmail.com

Wasimamizi wakuu:

Prof. Gladys Opinya, amaniwanjala@yahoo.com

Dr Edith Ngatia, edith_muthoni@yahoo.co.uk

Mwenyekiti, Hospitali ya Kenyatta / Chuo Kikuu cha Nairobi Kamati ya Maadili na

Utafiti, uonknh_erc@uonbi.ac.ke

121

APPENDIX X: Statement of Consent (English)

I...............................................................................................................................,

Parent/Guardian of........................................................................hereby permit Dr

Omuok Joyce Atieno to conduct a dental examination on my child. She has described

to me what is going to be done to my child, the risks, benefits involved and my

child‘s rights regarding this study. I also understand that the decision to participate in

this study will not alter my child‘s health status or treatment and my child will not be

forced to participate.

I understand that the examination is solely for academic purposes and doesn‘t imply

that any dental treatment will be offered and that my child shall be treated at the Lady

Northey Dental Hospital.

I am aware that although, the information obtained will be made available to Lady

Northey Dental Hospital, the University of Nairobi, Kenya, my child‘s details will

remain concealed and study results shall remain confidential.

I agree to photographs of my child‘s mouth being taken, and consent to their usage in

scientific publications. As a parent, I understand that by signing this form, I do not

waive any of my child's legal rights but merely indicate that I have been informed

about the research study in which am voluntarily agreeing my child participate.

YES NO

Child‘s Name: ........................................................................................................

Age: ......................................................................................................................

Parent/Guardian‘s Name: .....................................................................................

Signature/Thumb Print: .........................................................................................

122

Date: .....................................................................................................................

I do at this moment confirm that I have explained the nature of the study to the

patient and caregiver.

Name of Investigator: .....................................................................................

Signature of Investigator: .....................................................................................

Date: ....................................................................................................................

123

APPENDIX XI: Statement of Consent (Swahili)

IDHINI KUTOKA KWA MZAZI/MLEZI

Mzazi/mlezi …………………………… nimemruhusu Daktari Joyce Atieno Omuok

kutumia meno ya motto wangu kwenye utafiti wake. Nimesoma na kuelewa maelezo

ya utafiti wake. Naelewa kuwa matokeo ya utafiti huu yatasalia kuwa siri.

Nimeidhinisha Daktari Joyce Atieno Omuok kuchukua picha za mdomo wa motto

wangu na kutumika kwenye utafiti wake.

Nimekubali mtoto wangu kushiriki kwenye utafiti huu bila kulazimishwa na yeyote.

NDIO LA

Jina la motto: …………………………………………………………..

Umri: …………………………………………………………………..

Jina la mzazi/mlezi: ……………………………………………………

Sahihi: ………………………………………………………………..

Tarehe: ………………………………………………………………

124

APPENDIX XII: Data Collection Form

ID No.__________________________________ DATE OF BIRTH: --/--/-

Name of Hospital:_________________________ DD/MM/YY

SEX: Male Female

ANTHROPOMETRY

First Reading Second Reading Average

Height

Weight

ORAL HYGIENE STATUS: Plaque Index (Silness-Löe, 1964)

55

(M)

55

(B)

55

(D)

55

(P)

52

(M)

52

(B)

52

(D)

52

(P)

64

(M)

64

(B)

64

(D)

64

(P)

84

(M)

84

(B)

84

(D)

84

(L)

72

(M)

72

(B)

72

(D)

72

(L)

75

(M)

75

(B)

75

(D)

75

(L)

125

DENTAL CARIES ASSESSMENT

TOOTH STATUS CODE FOR DECIDUOUS TEETH

Sound A

Decayed B

Filled with D=decay C

Filled with no decay D

Missing as a result of caries E

Sealant varnish F

Bridge abutment or special crown G

55 54 53 52 51 61 62 63 64 65

85 84 83 82 81 71 72 73 74 75

126

STAGING, SCORING AND DETERMINATION OF THE DENTAL AGE OF

PANORAMIC RADIOGRAPHS

Tooth 31 32 33 34 35 36 37 Total

Maturity

Stage

Maturity

Score

Estimated

Age

Chronological

Age

Gender

127

APPENDIX XIII: Caregivers' Questionnaire (English)

A:SOCIAL DEMOGRAPHY

Date: __ /__ / __

Date of Birth of the child: __ / __ / __ Unknown

Sex: Male Female

Caregiver: Mother Father Aunty

Uncle Grandparent Other

Marital Status: Single Married Widowed

Separated Divorced Other

Residence:___________________________________________________________

Number of children:____________________________________________

How many people do you live with?__________________________________

Level of education of caregiver:

No formal education Primary School

Secondary School Technical college University

128

Occupation of Caregiver

Professional (accountant, doctor, teacher, company executives etc.)

Clerical and white collar Businessman/woman

Skilled manual worker

Unskilled worker Unemployed

MORBIDITY:

Natal History

Mode of delivery of the child:

Spontaneous natal delivery (SVD)

Caesarian section (CS) Unkown

Gestation period at delivery: _______________Weeks Unkown

Birth weight of the child: _______________kgs Unkown

Has the child received all immunisations? Yes No

Has your child suffered from any illness in the past seven days? Yes No

If yes, which of these illnesses:

A diarrhoea Cough/cold Malaria

Other (specify) _______________________________________________

Has the child had any chronic/prolonged illnesses in the past? Yes No

If yes, for what duration?_______________________________________months

At what age? _________________________________________________months

Trauma:

Has the child suffered from any trauma to the teeth/jaws in the past?

Yes No

129

If yes, at which location of the mouth?

Upper right Upper front Upper left

Lower right Lower front Lower left

At what age did the trauma occur? -

_____________________________________months.

C. DIETARY HABITS

Mode of feeding at birth:

Exclusive breastfeeding Breastfeeding and bottle feeding

Exclusive bottle feeding

Duration of breastfeeding (in months)

________________________________________

Duration of bottlefeeding (in months)

_________________________________________

What did you place in the bottle?

_______________________________________________________

Did your child sleep with the bottle in the mouth? Yes No

Did you breastfeed on demand? Yes No

What does your child feed on during the following meal times?

Breakfast:____________________________________________________________

10.00am snack:

____________________________________________________________________

Lunch

____________________________________________________________________

130

4.00pm snack

___________________________________________________________________

Dinner

___________________________________________________________________

131

APPENDIX XIV: Caregivers' Questionnaire (Swahili)

SEHEMU HII ITAJAZWA NA MZAZI/MLEZU WA MTOTO

Tarehe: __ /__ / __

Tarehe ya kuzaliwa: __ / __ / __ Haijulikani

Jinsia: Kiume Kike

Mlezi: Mama Baba Shangazi

Mjomba Babu/Nyanya Wengine

Hali ya ndoa: Hujaoa/hujaolewa Ndoa Mjane

Kutengana Talaka Nyingine

Makazi:_____________________________________________________________

Watoto wangapi?:____________________________________________________

Unaishi na watu wangapi?_____________________________________________

Kiwango chako cha masomo?:

Hakuna elimu rasmi Shule ya msingi

Shule ya sekondari Chuo cha ufundi Chuo kikuu

Kazi?:

Mtaalam (mhasibu, daktari, mwalimu, watendaji wa kampuni nk)

Kazi ya ofisi Mfanyibiashara Mtaalamu wa mwongozo wenye ujuzi

Mfanya kazi asiye na ujuzi Asiyeajiriwa

132

UGONJWA:

Historia ya mtoto:

Mtoto alizaliwa vipi?:

Kawaida: Upasuaji Haijulikani

Mtoto alizaliwa baada ya miezi mingapi?: Wiki _________ Haijulikani

Mtoto alizaliwa akiwa na uzito wa kilo ngapi?: Kilo ________Haijulikani

Mtoto amepewa chanjo zote? Ndio La

Mtoto ameugua ugonjwa wowote kwa wiki moja iliyopita? Ndio La

Ikiwa jibu ni ndio, ameugua ugonjwa gani?

Kuhara Homa/ kukohoa Malaria

Zingine (eleza)

____________________________________________________________

Mtoto ameugua ugonjwa uliochukua mda mrefu?

Ndio La

Ikiwa ndio, ilichukua mda gani? Miezi____________________________________

Aliuguwa akiwa na umri gani?? Miezi _____________________________________

133

Kiwewe:

Mtoto amepata maumivu yanayotokana na kuumiza/kuanguka kwenye

meno/ufizi?

Ndio La

Ikiwa ndio, ni sehemu gani ya mdomo ilihusika?

Upande wa juu kulia Upande wa juu mbele Upande wa juu kushoto

Upande wa chini kulia Upande wa chini mbele Upande wa chini kushoto

Aliumia akiwa na umri upi?

Miezi _____________________________________________

C. MALAZI/ VYAKULA

Mtoto alipozaliwa, alinynya au alipewa maziwa kwa chupa?

Kunyonya pekee

Kunyonya pamoja na maziwa kwa chupa

Maziwa kwa chupa pekee

Mtoto alinyonya kwa mda wa miezi ngapi? ________________________

Mtoto alitumia chupa kwa mda gani? _____________________________

Mtoto aliwekewa nini kwenye chupa ______________________________

Mtoto alilala akiwa na chupa mdomoni? Ndio La

Mtoto alinyonya kila alipotaka? Ndio La

134

Mtoto wako hula nini kwa wakati zilizotajwa hapo chini?

Chakula cha asubuhi? __________________________________________________

Saa nne? ____________________________________________________________

Chakula cha mchana? __________________________________________________

Saa kumi?____________________________________________________________

Chakula cha jioni/usiku?________________________________________________

135

APPENDIX XV: Demirjian's Tooth Maturity Chart

Adapted from Liversidge, H. M. (2012) (82)

136

APPENDIX XVI: Demirjian’s Tooth Stage Descriptions

Adapted from Liversidge, S. M. (2012) (82)

137

APPENDIX XVII: Tooth Notation

I1 Permanent mandibular central incisor

I2 Permanent mandibular lateral incisor

C Permanent mandibular canine

P1 Permanent mandibular first premolar

P2 Permanent mandibular second premolar

M1 Permanent mandibular first molar

M2 Permanent mandibular second molar

138

APPENDIX XVIII: Demirjian’s Tables (Maturity Scores)

Adapted from A. Demirjian and H. Goldstein (1976) (36)

139

APPENDIX XIX: Certificate of Originality

140

APPENDIX XX: Declaration of Originality